{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AI股票拟合算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import tensorflow as tf\n",
    "import pandas            as pd\n",
    "import tensorflow        as tf  \n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "from numpy                 import array\n",
    "from sklearn               import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models          import Sequential\n",
    "from tensorflow.keras.layers          import Dense,LSTM,Bidirectional\n",
    "\n",
    "from numpy.random   import seed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保结果尽可能重现\n",
    "\n",
    "seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 设置相关参数\n",
    "n_timestamp  = 5    # 时间戳\n",
    "n_epochs     = 30    # 训练轮数\n",
    "# ====================================\n",
    "#      选择模型：\n",
    "#            1: 单层 LSTM\n",
    "#            2: 多层 LSTM\n",
    "#            3: 双向 LSTM\n",
    "# ====================================\n",
    "model_type = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pro_api in module tushare.pro.data_pro:\n",
      "\n",
      "pro_api(token='', timeout=30)\n",
      "    初始化pro API,第一次可以通过ts.set_token('your token')来记录自己的token凭证，临时token可以通过本参数传入\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pro=ts.get_hist_data('603722')\n",
    "help(ts.pro_api)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 初始化pro接口\n",
    "pro = ts.pro_api('8eb598af64eb0ae48be918215795fac458e509b4166440f08923f43e')\n",
    "\n",
    "# 拉取数据\n",
    "data = pro.daily(**{\n",
    "    \"ts_code\": '000768.sz',\n",
    "    \"trade_date\": \"\",\n",
    "    \"start_date\": \"2000-1-1\",\n",
    "    \"end_date\": \"\",\n",
    "    \"offset\": \"\",\n",
    "    \"limit\": \"\"\n",
    "}, fields=[\n",
    "    \"trade_date\",\n",
    "    \"open\",\n",
    "    \"close\",\n",
    "    \"high\",\n",
    "    \"low\",\n",
    "    \"vol\",\n",
    "    \"ts_code\"\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close        vol\n",
      "0     000768.SZ   20230111  24.53  24.88  24.16  24.31  202988.92\n",
      "1     000768.SZ   20230110  24.80  24.92  24.31  24.49  180475.47\n",
      "2     000768.SZ   20230109  25.70  25.75  24.60  24.77  337391.53\n",
      "3     000768.SZ   20230106  25.65  25.94  25.55  25.76  136960.21\n",
      "4     000768.SZ   20230105  25.55  25.84  25.52  25.74  117999.58\n",
      "...         ...        ...    ...    ...    ...    ...        ...\n",
      "5405  000768.SZ   20000110  14.50  14.80  13.88  14.73   39234.00\n",
      "5406  000768.SZ   20000107  13.69  14.53  13.69  14.29   38146.00\n",
      "5407  000768.SZ   20000106  13.44  13.73  13.20  13.65   11130.00\n",
      "5408  000768.SZ   20000105  13.58  13.75  13.19  13.53   11894.00\n",
      "5409  000768.SZ   20000104  13.65  13.68  13.18  13.60   15667.00\n",
      "\n",
      "[5410 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "print(data)\n",
    "data.to_csv(\"stock000768.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-01-11</th>\n",
       "      <td>24.53</td>\n",
       "      <td>24.88</td>\n",
       "      <td>24.16</td>\n",
       "      <td>24.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-10</th>\n",
       "      <td>24.80</td>\n",
       "      <td>24.92</td>\n",
       "      <td>24.31</td>\n",
       "      <td>24.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-09</th>\n",
       "      <td>25.70</td>\n",
       "      <td>25.75</td>\n",
       "      <td>24.60</td>\n",
       "      <td>24.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-06</th>\n",
       "      <td>25.65</td>\n",
       "      <td>25.94</td>\n",
       "      <td>25.55</td>\n",
       "      <td>25.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-05</th>\n",
       "      <td>25.55</td>\n",
       "      <td>25.84</td>\n",
       "      <td>25.52</td>\n",
       "      <td>25.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>14.50</td>\n",
       "      <td>14.80</td>\n",
       "      <td>13.88</td>\n",
       "      <td>14.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>13.69</td>\n",
       "      <td>14.53</td>\n",
       "      <td>13.69</td>\n",
       "      <td>14.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>13.44</td>\n",
       "      <td>13.73</td>\n",
       "      <td>13.20</td>\n",
       "      <td>13.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>13.58</td>\n",
       "      <td>13.75</td>\n",
       "      <td>13.19</td>\n",
       "      <td>13.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>13.65</td>\n",
       "      <td>13.68</td>\n",
       "      <td>13.18</td>\n",
       "      <td>13.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5410 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             open   high    low  close\n",
       "trade_date                            \n",
       "2023-01-11  24.53  24.88  24.16  24.31\n",
       "2023-01-10  24.80  24.92  24.31  24.49\n",
       "2023-01-09  25.70  25.75  24.60  24.77\n",
       "2023-01-06  25.65  25.94  25.55  25.76\n",
       "2023-01-05  25.55  25.84  25.52  25.74\n",
       "...           ...    ...    ...    ...\n",
       "2000-01-10  14.50  14.80  13.88  14.73\n",
       "2000-01-07  13.69  14.53  13.69  14.29\n",
       "2000-01-06  13.44  13.73  13.20  13.65\n",
       "2000-01-05  13.58  13.75  13.19  13.53\n",
       "2000-01-04  13.65  13.68  13.18  13.60\n",
       "\n",
       "[5410 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"C:\\\\Users\\\\123\\\\Desktop\\\\stock000768.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")[[\"open\",\"high\",\"low\",\"close\"]]\n",
    "# data=pd.read_csv(\"./stock688333.csv\",parse_dates=[\"trade_date\"])[[\"trade_date\",\"open\",\"high\",\"low\",\"close\",\"vol\"]]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 30 entries, 0 to 29\n",
      "Data columns (total 6 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   index   30 non-null     int64         \n",
      " 1   date    30 non-null     datetime64[ns]\n",
      " 2   open    30 non-null     float64       \n",
      " 3   high    30 non-null     float64       \n",
      " 4   low     30 non-null     float64       \n",
      " 5   close   30 non-null     float64       \n",
      "dtypes: datetime64[ns](1), float64(4), int64(1)\n",
      "memory usage: 1.6 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3791</td>\n",
       "      <td>2023-01-05</td>\n",
       "      <td>49.2</td>\n",
       "      <td>49.50</td>\n",
       "      <td>48.50</td>\n",
       "      <td>48.93</td>\n",
       "      <td>47.486</td>\n",
       "      <td>46.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3792</td>\n",
       "      <td>2023-01-06</td>\n",
       "      <td>49.5</td>\n",
       "      <td>49.83</td>\n",
       "      <td>49.05</td>\n",
       "      <td>49.74</td>\n",
       "      <td>48.270</td>\n",
       "      <td>46.933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3793</td>\n",
       "      <td>2023-01-09</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.95</td>\n",
       "      <td>49.70</td>\n",
       "      <td>50.91</td>\n",
       "      <td>49.052</td>\n",
       "      <td>47.460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3794</td>\n",
       "      <td>2023-01-10</td>\n",
       "      <td>50.9</td>\n",
       "      <td>50.91</td>\n",
       "      <td>49.14</td>\n",
       "      <td>49.21</td>\n",
       "      <td>49.464</td>\n",
       "      <td>47.915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3795</td>\n",
       "      <td>2023-01-11</td>\n",
       "      <td>49.5</td>\n",
       "      <td>51.00</td>\n",
       "      <td>49.44</td>\n",
       "      <td>50.70</td>\n",
       "      <td>49.898</td>\n",
       "      <td>48.427</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index       date  open   high    low  close       5      10\n",
       "25   3791 2023-01-05  49.2  49.50  48.50  48.93  47.486  46.547\n",
       "26   3792 2023-01-06  49.5  49.83  49.05  49.74  48.270  46.933\n",
       "27   3793 2023-01-09  50.0  50.95  49.70  50.91  49.052  47.460\n",
       "28   3794 2023-01-10  50.9  50.91  49.14  49.21  49.464  47.915\n",
       "29   3795 2023-01-11  49.5  51.00  49.44  50.70  49.898  48.427"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import akshare as ak\n",
    "\n",
    "pingan = ak.stock_zh_a_daily(symbol=\"sh601318\", adjust=\"qfq\")\n",
    "df3 = pingan.reset_index().iloc[-30:,:6]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "df3 = df3.sort_values(by='date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "\n",
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAAOJe5y0tV9tHfVXVwf502wxoi20WXKerqG2XtGNcG1QHBEfSAtaqqSs8995wcDocmTZqk4cOH1+lz9OhRSVJKSooiIyN9zpOQkOB5X15e3qyajhw5orKyMknSqFGjfPapeVhVevrpp/9lZmbK7XbXO85qtWrw4MFyOBzau3dvs2oEAAAAAAA4m5hOp8o/Wyt32ak6bSHJXRV9/U8V3j9NhqXVbqgGWkXQ/6Jfe+01HT58WP369dOtt97qs4/1P9vBw8LC6p2nuLj6KXMhISHq0KFDs2qqOQc2JCREvXv39tmnR48ekk6Hv1L1+auSlJiYqLg43//SUjMuJyenWTUCAAAAAACcLUzTlH3bRrkKT3hdN0LDFTHySkWOv16WDjFtVB0QXEF9yNW2bdu0du1adejQQbNmzfIEqWfq3Ln6SXGHDh3yHBVwpq+/rn7a3KBBgxQaGtqsuk6dqv6XlMTExHpr6tixoyTp+PHjdcZ16dKl3rljYmLqjPNXQUFBo31iY2PrrRkAAAAAAJxnPmkfD26qzNxd51gAS4eOippwgyyR0fWMAs4NQQtY8/PztXjxYhmGoXvvvVfx8fH19h0xYoTeeOMNlZWV6YMPPtCNN97o1Z6RkaFt27ZJkiZOnNjs2pxOpyQpKiqq3j41bSdPnvRcqzlXtaFxNeey1h7nr5kzZzbaZ/HixQ3+LAEAAAAAAFqT89j3qti5xeuaERKqyCuvIVzFeSEoRwS4XC796U9/UllZmW688Uaf567WlpCQoClTpkiS3nrrLb377rsqLCxUXl6e/vnPf2rBggWSqm+/HzFiRLPrs/znrI+GjiQICanOnisrK5s9DgAAAAAA4FzkOlWs8n+vl0zT63rEZeN5kBXOG0HZwbp8+XLt27dP/fr10y233OLXmIkTJyohIUHLli3T8uXLtXz58jp97rjjDhmG0ez6agLShm61r1mndlAaHh4e0Dh/LV68uNE+sbGxTZ4XAAAAAACgpZlVVSr/bK3MSofX9fDBIxTavWfbFAW0gRYPWHfv3q333nuv0XNXfRk+fLiGDx+uwsJCFRYWyul0av78+f+fvTsPjqs804Z/nXN619aSWlLLtrxIsiXbjGVMMDZeZcSSTBjyFplUUqnApKgEA0mV36nJVKpevkwCmaJSMwzky1CkUvGU/U1IQiCT5GUyYBu8CAzY4Fje8CJbXiW1urWr97M83x9tyWp3a7G1nJb6+lVRiGdp3RJaum89z30jEongrrvuwooVKyYlxvFc4w+FQgASRZonum+8ePWfiIiIiIiIiGYCIQQiH+2D0dedNG6tWAT7HaPfZCaabSY9wdrY2AghBAYGBkatKRoIBPCVr3wFAPD0009j8+bNQ3NFRUUoKirCm2++iUgkAqvVir/7u7+btBg9Hg+A0RtR9fb2AgAcDseE9xERERERERHRzNHka0o7HowHx1yTLWIn/wL16sWkMbmgCM619ZNy+5hoJpn0GqyKosBqtY74z/ATrYNjg7VNh+vv78dbb70FAHjkkUdQVlY2aTFWVFTAZrMhFouhtbU17Zrm5mYAyadKq6qqAADt7e2IRCJp9507dy5lHxERERERERHRbKFevYTY8U+TxiSbHa5ND0CyWk2Kisg8k36CdevWrdi6deuI86dOncKPfvQjlJSU4JVXXhlx3eDpVa/Xiy996UuTGqPVasXSpUtx7NgxHDx4cOgk7XAnTpwAACxatGhozOPxoLy8HO3t7Th06FDSqVsAMAwDp06dAgBUVlZOasxERERERERENHX2Pb4P9TvrR10z/NTqSu/KMR9vNtJ7uxH5cG/yoCTBtb4BSl6BOUERmWxKmlxN1OXLl7F7924AwBNPPDHUlGoybdq0CceOHcPbb7+NhoYGFBUVDc2dPHly6ATrqlWrUvb99re/xZtvvok1a9YklQLYv38/enp6IMsy6urqJj1mIiIiIiIiIpo6YyVFhydgZ2sCdTRGLIpw424ITU0ad9y5BpbyeSZFRWS+SS8RMBn+4z/+A4Zh4N577x0zURkIBPD1r38dX//6hmfWvwAAIABJREFU19HY2Dju97FmzRqUlZUhFArhueeew5kzZxCPx3H48GH827/9GwCgtrYW1dXVSfsaGhqQk5MDv9+P559/HpcvX0Y0GsXevXuxfft2AMC6detQWFh4ix81EREREREREVFmEoaByMH3YAz0JY1bFy2BrfavTIqKKDNk3AnWxsZGnD59Gjk5OeNqbCWEgKom/nJiGMa434/FYsG2bdvw4x//GG1tbfjBD36QNJ+Tk5O21EF+fj6++93v4sUXX0RzczO+973vJc2XlJTgscceG3ccRERERERERESZLtZ0GFr7taQxpbgUztUb2NSKsp4khBBmB2Emn8+HHTt24OjRoxj8VCxfvhzf+ta3MGfOnBH3tbS0YMeOHThz5gwAQJIk3H333XjiiSd4epWIiIiIiIhoFsrWEgHxi80pdVclhwu5n/9fkF25JkVFlDmyPsE6qLe3F36/H8XFxSguLh73vs7OTnR3d6OsrAwFBSzmTERERERERDRbZWOCVe8KILjnT4Cu3xiUFeQ0fBGWEq95gRFlkIwrEWAWt9sNt9t9y/s8Hg88Hs8UREREREREREREZB4jEka4cVdychWA83PrmFwlGiYjm1wREREREREREZF5hK4j/P4eGOFQ0rht8XLYFi81KSqizMQEKxERERERERERJYke+RB6wJc0ppSWw3HXWpMiIspcTLASEREREREREdGQePNniDd/ljQmu3Lh2nA/JEUxKSqizMUEKxERERERERERAQA0fzsinxxMGpMUC1ybHoTscJoUFVFmY4KViIiIiIiIiIhghIIIv78HEEbSuGPNJihFbPBNNBImWImIiIiIiIiIspzQNIQbd0FEI0nj9mUrYVtYbVJURDMDE6xERERERERERFlMCIHIoUbo3Z1J45Y582Gvu9ukqIhmDiZYiYiIiIiIiIiyWPz0caiXmpPG5LwCuNZtgSQzdUQ0Fn6XEBERERERERFlKbXtKqJNh5LGJKsNrk0PQrLZTYqKaGZhgpWIiIiIiIiIKAvpA32IfPAeIMSNQUmC8956KAWF5gVGNMNYzA6AiIiIiIiIiChFff2Nt/ftMy+OWUqocYQP7IJQY0nj9hWfg3XeQnOCIpqheIKViIiIiIiIiCiLCCEQ+XAfjL6epHHr/ErYl99pUlREMxcTrEREREREREREWSR2/AjUa5eSxhR3MZxrNkOSJHOCIprBmGAlIiIiIiIiIsoS6pWLiJ08kjQm2exwbXwAktVqUlREMxtrsBIRERERERHR9BteYzWdpqbxr2WN1nHRe7oQ+eimz5Ukw7Xhfsh5+eYERTQLMMFKRERERERERNOrvj45gZpOMHjj7bHW1tczyToGIxZFuHE3hKYmjTtWrYHFO9ekqIhmB5YIICIiIiIiIiKaxYRhIPLBuzCC/Unj1soa2GruMCkqotmDJ1iJiIiIiIiIyDwrV6YfH35qdTxraETRo4eg+VqTxpTiUjhXr2dTK6JJwAQrEREREREREdE47Ht85pUhiLecQ/zM8aQxyeFKNLVSmBYymxACPWEVRTk2s0OhCWCJACIiIiIiIiKiWUjr9CNyuDF5UFbg2ng/ZFeOOUHREM0w0HihE78/1orW3ojZ4dAEMMFKRERERERERDTLGJEwIu/vBnQ9ady5ej0sJV6ToqJBobiGP5/04WzHAIQQeO+sH/1RdeyNlJGYYCUiIiIiIiIimkWEriHcuBtGOJQ0bluyHLaqWpOiokEdA1H84VgbOgaiQ2NRTceeM34YQpgYGd0uFtsgIiIiIiIiIpolhBCIfnIQemdH0rildA4cq9aaFBUNOtsxgPcvdKYkUi2yjJXzCiCz6diMxAQrEREREREREdEsEW/+DPELZ5LG5Jw8ODfcD0lRTIqKdEPg40vdONXelzKXa7figdpSeHLtJkRGk4EJViIiIiIiIiKiWUDraEP00w+TxiTFAtfGByA7HCZFRVFVx7tn/WjrS21kNafAiYaaUjisTH7PZEywEhERERERERHNcEZwAOH39wDCSBp3rt0MpchjUlTUGYxh9xk/grHUBlZ3zCnAmoVFLAswCzDBSkREREREREQ0gwlNQ/j93RCxaNK4ffmdsC6oMikqutAZxIHmTmhGctJbkSWsr/SgpizPpMhosjHBSkREREREREQ0QwkhEPl4P/TuzqRxy5z5sK/4nElRZTdDCHx6pQdN13pT5lw2Cx6oLUNpHuutziZMsBIRERERERERzVDxz45BvXwhaUzOd8O1bgskWTYpquwV03TsPRfA1Z5wylxZngMNtaXIsTEdN9vw/ygRERERERER0Qyktl5B9NjhpDHJaoNr44OQbDwhOd16w3HsOtOBvkhqvdXasnzcW1kEC5PesxITrEREREREREREM4ze34vIwb2AEDcGJQnOdVugFLjNCyxLXe4OY+85P1Q9ud6qJEm4d1ExlnnzILGZ1azFBCsRERERERER0Qwi1DjCjbsh1FjSuGPF3bDOXWBSVNlJCIGj13px5EovBETSnNOqoKGmFOUFTpOio+nCBCsRERERERER0QwhhED44F4YfT1J49b5VbAtX2lSVNlJ1Q3sbw7gYlcoZc6TY8cDS8uQa2fqLRvw/zIRERERERER0QwRO34EWuvlpDHFXQznmk28gj6N+qMqdp/uQHc4njJX7cnFxmoPLArrrWYLJliJiIiIiIiIiGYA9cpFxE4eSRqT7A64Nj4AyWo1Karsc603gvfO+hHT9KRxCRJWLyzCijn5THZnGSZYiYiIiIiIiIgynN7bjchH+5IHJRmu9Q2Q8/LNCSrLCCFwoq0fhy51p9RbtVlk3LekFBWFLpOiIzMxwUpERERERERElMGMWBThA7sgNDVp3LFqDSzeuSZFlV003cD7F7rQHBhImSt02fBAbRkKnDxFnK2YYCUiIiIiIiIiylDCMBA5+B6MYH/SuLWyBraaO0yKKrsEYxp2n+lAZzCWMrewOAebq0tgs7Deajab9gTrxYsX8eyzz8LtduOVV15Jmf/444+xZ88etLW1ob+/HwUFBZg7dy4aGhpwzz33THo8Pp8Pb7zxBo4fP45wOIy5c+fi4YcfxoYNG0bd19LSgt///vc4ffo04vE4Fi1ahEcffRQrV7JjHxERERERERFNjljTYWjt15LGlOJSOFevZ53PaeDrj2LPGT8iqpYyd9f8Qqya5+b/B5reBGs0GsXLL78MVVXTzv/617/GH//4x6H/liQJnZ2d6OzsxLFjx7Bx40Y888wzk/aFe/HiRfzwhz9EJBIZen+XLl3Cz372M3R0dODLX/5y2n1NTU34yU9+Al3Xh/adPXsWL7zwAp566ils3rx5UuIjIiIiIiIiouwVv9iM2OljSWOSwwXXxvshKbyUPNVO+/pxsKULhkiut2pVZNQvLsHC4hyTIqNMM63fjb/85S/R3t6edu7kyZP44x//iJycHDz55JOoq6uDzWZDIBDAnj178NZbb6GxsRErVqzAxo0bJxxLKBTCCy+8gEgkgrKyMjzzzDNYsmQJWlpa8NJLL+GNN95AXV0dFi9enLTP5/PhxRdfhK7rqKysxFNPPYWKigqcOHECL7/8MrZv345ly5ahtLR0wjESERERERERUXbSuwKIHDqQPCgrcG28H7Ir15ygsoRuCHx4sQunff0pc/kOKx5cWoZCl82EyChTTVuBiP3796OxsXHE06eNjY0AgEcffRRr1qyB0+mEoijwer34xje+gdWrVwMAjhw5Minx/OEPf0Bvby/sdjueffZZ1NbWQpZlVFdX4zvf+Q6EENixY0fKvt/85jeIxWIoLCzEs88+iwULFkCWZdTV1eGb3/wmYrEYfv3rX09KjERERERERESUfYxoBOHG3cD1m7ODnJ9bB0uJ16SoskM4ruHPp9rTJlfnuV340oo5TK5SimlJsLa1tWH79u2QJAlf/OIX067p7u4GAHi96X9QeDweAEA8Hp9wPLqu47333gMANDQ0oKysLGl+6dKlqK6uRnNzMzo7O4fG+/v7cejQIQDAI488gtzc5L8YrVu3DoWFhThy5MiIZRCIiIiIiIiIiEYidB3h9/fACAeTxm2Ll8O2eKlJUWWHQDCGPxxrg68/mjJXN9eNh5aVwWFVTIiMMt2UJ1hVVcVLL72EWCyGhx9+GKtWrUq7rqioCABw9OjRlDlN03DsWKLmSGVl5YRjunr1KkKhEABg7dq1adcMNqtqamoaGjtz5gwMwxhxn6IoWLFiBWKxGE6fPj3hOImIiIiIiIgou0SPfATdn1xeUSkth+Ou9PkLmhzN/iD+74k2hOLJzawssowtS0pxz8IiyGxmRSOY8gTrzp07cfnyZdTU1OBrX/vaiOu2bNkCWZbx7rvv4ne/+x0CgQBisRguXryIf/3Xf0Vrayvy8/PR0NAw4ZgG68BaLBZUVVWlXbNgwQIAidO3g3w+HwCgtLQUhYWFo+5rbW2dcJxERERERERElD3izacRbz6VNCa7cuHacD8khScnp4IhBD662IV9zX7oRnIzq1y7BX/zV+WoLmHNWxrdlDa5Onz4MHbv3o28vDxs27YNyig/DGpra/H3f//3+MUvfoE333wTb775ZtL84sWL8fTTT4+Y2LwVAwMDABKJ0pFiKigoAAD4/f6UfeXl5SM+dn5+fsq+8erq6hpzjdvtHvXzSEREREREREQzj+b3IfLpweRBRYFr4wOQHU5zgprloqqOvef8uNYbSZnz5jtwf00ZnDbmYGhsU5Zg7ezsxKuvvgpJkvDMM8+guLh4zD2hUAiapqWds9ls6O7uxty5cycc2+D7yMnJGXHN4FxPT8/Q2GBd1dH2DdZlHb5vvJ566qkx17z66qvj+lwSERERERER0cxghIMIv78HMG5qanXPJijFJSZFNbt1h+LYdaYDA9HUHjrLy/OxZmExFJklAWh8piTBqus6fvrTnyIUCuGRRx4Zse7qcI2NjXj11VcBAJIkoaKiArm5uWhvb0dPTw9OnTqFzz77DE8++SS2bNkyofhkOVEZwWYbueubxZL41AxvqnW7+4iIiIiIiIiI0hG6hnDjHohoOGncvrQOtkWLTYpqdrvYFcL+5gBU3UgalyUJ66s8qC3LMykymqmmJMH6+uuv4+zZs6ipqcFXv/rVMddrmob//M//BABUVFRg27ZtqKioAJBI1u7btw87duxAPB7Ha6+9hnXr1sFut992fIMJ0tGu2kvXCxcPT5QOvs9b3Tdegwnm0bjd7lt+XCIiIiIiIiLKPEIIRA5/AL0rucygpXwe7CtXmxTV7CWEwJGrvfjL1dRbxy6bBQ01pfDmO0yIjGa6SU+wnjhxAn/605/GVXd10Llz59DX1wdFUfAP//APSTVOFUUZamz1i1/8AgMDA7hw4QKWLVt22zGO5xp/KBQCkPjmm+i+8eLVfyIiIiIiIqLsET97EmrL2aQxOTcfznX3QZKnvC95VolrBvY1+3G5O5wyV5rnwP21pcixTWmrIprFJv0rp7GxEUIIDAwMjFpTNBAI4Ctf+QoADJ1ynTt37ogNpNasWYNf/OIXAG6vvulwHo8HwOiNqHp7ewEADseNv1zc7j4iIiIiIiIiouG09muI/uXjpDHJYoVr04OQ7cwpTCb/QAx7z/nRn6beak1pHtZVFcPChDZNwKQnWBVFgdVqHXHeMAzoeqJo8+C6oqKiob3jMVqTqfGoqKiAzWZDLBZDa2tr2sZZzc3NAJJPlVZVVQEA2tvbEYlE4HSmdvE7d+5cyj4iIiIiIiIiokHGQD/CH7wLiOQaoM619VDcRSZFNfsYQuB4ax8+udKTctNYkiSsXViE5eX5Q+UeiW7XpCdYt27diq1bt444f+rUKfzoRz9CSUkJXnnlFQBAW1sbgETiUlXVtAnakydPAkgkYaurqycUo9VqxdKlS3Hs2DEcPHhw6CTtcCdOnAAALFq0aGjM4/GgvLwc7e3tOHToEDZv3py0xzAMnDp1CgBQWVk5oRiJiIiIiIiIaPYRqopw426IeCxp3H7HXbDOXzTCLrpVwZiG/c0BtPVFUuYcFgX31ZRirjv14BzR7ciI889z5szB4sWLEY1G8fbbb6fM9/f347XXXgMAbNmyZagW6kRs2rQJAPD222+ju7s7ae7kyZNDJ1hXrVqVdt+bb76JaDSaNLd//3709PRAlmXU1dVNOEYiIiIiIiIimj2EEIh8fAB6b1fSuGXuAthX3GVSVLPPxa4Qft/Umja5WprnwP+qm8PkKk2qjKne+53vfAfPP/88fvWrX6GpqQk1NTWwWCxDp0VjsRgqKyvxjW98I2lfIBDAtm3bAABPPvkkNm7cOK73t2bNGrz++uvo6OjAc889h61bt6KyshJNTU34+c9/DgCora1NOS3b0NCAt956C36/H88//zy+/e1vo6ysDB9++CG2b98OAFi3bh0KCwsn+ikhIqLxqK+/8fa+febFQUREREQ0hvipJqhXLiSNyQWFcK3bwmvqk0DTDXx0qRunff0pcxIk3FnhxqoKN2R+rmmSZUyCtby8HP/yL/+C3bt349NPP8U777yDSCQCh8OBiooKrFmzBg899BBsNlvSPiEEVDVRpNgwjHQPnZbFYsG2bdvw4x//GG1tbfjBD36QNJ+Tk5O21EF+fj6++93v4sUXX0RzczO+973vJc2XlJTgscceG3ccRERERERERDT7qa2XET3+SdKYZLXDtfEBSFbbCLtovDqDMew9F0BvJJ4yl2u3oH5xCcoLeGqVpoYkbq7ym2V8Ph927NiBo0ePDhU8Xr58Ob71rW9hzpw5I+5raWnBjh07cObMGQCJ4sh33303nnjiCZ5eJSKaTjzBSkRERDTz1NcDTU2Jt1euTL9mcH48a1auzOjngnpfL0K7/gChDkv+SRJcmz8P65wK8wKbBYQQONHWj8OXu2GkSXFVenKxvrIYDuv4GqsT3Y6MOcFqFq/Xi+9///vo7e2F3+9HcXExiouLx9xXWVmJ5557Dp2dneju7kZZWRkKCgqmIWIiIiIiIiIimilEPIZw467k5CoAR91qJlcnKBzXsL+5E9d6wylzVkXGvYuKsaQ0l+UXaMplfYJ1kNvthtvtvuV9Ho8HHo9nCiIiIiIiIiIioplMCIHwh/tg9PcmjVsXVMG2jM2xJ+JKTxgHmgOIqHrKnCfXji1LSuF2Wk2IjLIRE6xERERERERERFMgdvxTaK2Xk8aUwmI412zmqcrbpBkGDl3qwan2vrTzdXPd+Nz8QigyP780fZhgJSIiIiIiIiKaZOqVFsRO/iVpTLI74Nr4ICQL0zG3ozsUx95zfnSHUxtZuWwWbF5cgnluNrKi6cfvaCIiIiIiIiKiSaT3dCHy0f7kQUmGa8P9kHPzTIlpJhNC4DPfAD6+1AXdSG1ktaDIhU3VJWxkRaZhgpWIiIiIiIiIaJIY0SjCjbshNDVp3LFqLSxlc0yKauaKqjoOnA/gcndqIytFlrBmYTGWefNYcoFMxQQrEREREREREdEkEIaByMF3YQT7k8ZtlTWw1Sw3KaqZ61pvBPubAwjHtZS5IpcNW5aUoijHZkJkRMmYYCUiIiIiIiIimgTRo4eg+VqTxhRPGRyr1/OE5S3QDYFPr/TgeGsfBFJLAiwvL8A9CwthkWUToiNKxQQrEREA1NffeHvfPvPiICIiIiKiGSnecg7xM8eTxiRnDlwb7oekMP0yXr0RFXvP+dEZjKXMOa0KNi0uwfxClwmREY2M3+FERERERERERBOgdwUQOdyYPCgrcG28H7Irx5ygZhghBM75gzjY0gXNMFLm57ld2LzYA5eNqSzKPPyqJCIiIiIiIiK6TUYkjHDjLkDXk8add6+HxVNmUlQzS1TV8UFLF1o6gylzsiThnoVFuKM8n2UWKGMxwUpEREREREREdBuEriP8/h4Y4VDSuG3Jctiqa02KamZp74tgX3MAwVhqIyu304YtS0rgybWbEBnR+DHBSkREmW14fdx0mprGvxZgjV0iIiIimjTRIx9CD/iSxiylc+BYtdakiGYO3RD4y9UeNF1L38hqqTcfaxcWwaKwkRVlPiZYiYgoc9XXJydQ0wkOu0Y01trBx2SSlYiIiIgmKN78GeLNnyWNya5cODc0QFIUk6KaGfqjKvaeC8A/EE2Zs1sUbKz2YFExa9fSzMEEKxERERERERHRLdD8PkQ+/TBpTFIscG16ELLDaVJUM0OzP4gPWjqh6qmNrOYUOLF5cQly7UxX0czCr1giIpoZVq5MPz781OpIa25eR0RERER0m4xwEOH39wBGclMrx5pNUIo8JkWV+eKagQ9aOnE+kNrISpIk3D2/ECvmFkBmIyuagZhgJSIiIiIiIiIaB6FpCB/YDRENJ43bl9XBtrDapKgyX8dAFHvPBTAQVVPm8h1WbFlSitI8NrKimYsJViIiIiIiIiKiMQghEDn8PvTuQNK4pbwC9rrVJkWV2Qwh0HStF0eu9kKI1EZWS0rzcO+iYtgsbGRFMxsTrEREREREREREoxBCIHb8CNSL55LG5bwCONdtgSQzQXizYEzD3nN++PpTG1lZFRkbqjyoLsk1ITKiyccEKxERERERERHRCISuIfJxI9RLzUnjksUK18YHINsdJkWWuS52hXDgfABxLbWRlTffgfrFJchzWE2IjGhqMMFKRJSh6nfWD7297/F9JkZCRERERJSdjEgY4cbd0Ds7Uuac926B4i4yIarMJYTAp1d6cPRab8qcBAmrKty4s8LNRlY06zDBSkRERERERER0E727E+EDu2CEb+p6L0lw3HUvrBULTYkrU6m6gf3NAVzsCqXM5dqt2LKkBN58nval2YkJViKa/errx17T1DT+9fsm5zTp8BOq6TT5bsQ01lqecCUiIiIimjzqtUuIHNwLoSV3vZcsVjjX3wfr3AUmRZaZgjENu093oDMUS5mr8uRifVUx7BbFhMiIpgcTrEQ0u9XXJydPRxIc9lfpsdbX1084yVq/sz4pgZo2pPiNmMZaW7+znklWIiIiIqIJEkIgfvoYok2HgZu63ss5eXBtfohlAW7SMRDF7tN+RFQtaVySJNy7qBjLvHmQWBKAZjkmWImIiIiIiIgo6wldQ+TwB1BbzqbMKSXeREMrh9OEyDLX+UAQB84HoBvJyWibRUbDklLMK3SZFBnR9GKClYiyx8qVI88NP7U60rrxnIS9DSu96d/f8FOr41lDRERERES3x4hGEs2sAr6UOeuiJXDeswGSwhTKoNGaWRU4rXiwtgxul82EyIjMwZ8ORERERERERJS19N5uhPe/AyM0kDwhSXDUrYZtWR2vuA+j6gb2NQdwKU0zq7kFTtxXUwqHlfVWKbswwUpEREREREREWUltvYLIwfcg1HjSuKRY4Fx3H6wVC80JLEMFYxp2ne5AV5pmVsu8+Vi7qBiKzGQ0ZR8mWImIiIiIiIgoqwghED9zAtGjH6c2s3LlwLXpIShFHpOiy0xjNbNaXp5vUmRE5mOClYiIiIiIiIiyhtB1RD89iPj50ylziqcs0czKyeZMwzX7g2i8MEIzq5oyzHOz+RdlNyZYiWh61dffeHvfPvPiICIiIiKizDBSM9lgcOw1t8iIRhF5fw80f1vKnHVhNZz3bIJkYapkkBACn1zpQdNIzayWeuF2Wk2IjCiz8KcGEU2u4QnUdIY/MRprLTC5SdjRnpRNwZM3IiIiIiLKHHpfL8IH3oEx0JcyZ6+7G/bld7KZ1TCqbmDfuQAudac2s5rndmLLEjazIhrEBCsRTZ76+rGTk7eayKyv50lXGpWhyNCtChRVh2x2MMPU77zxB4R9j/NrmIiIiCjJvn3jO3BxK483Cq39GsLvvwuhJjdnkhQLnPfWwzq/cvJimQUGoip2ne5AdzieMre8vABrFxVBZjKaaAgTrEQ0u433idvwZO/KlWM/JmWEmKwjWl4AXH9upygqbEKGxZAhg0/4iKYa/5BARDSLmFHKa5reT+zsSUSPfAQII2lccuYgZ9ODUIpLpiWOmcLXH8WeMx2IqHrSuCRJWFdZjGVeNrMiuhkTrEQ0NUZKUo43kTmZ1/TH88SNtWFnnJisIaroGJ5H1WUDERiADFiEDKshwypkSEy2EhEREWUdYRiIfvoh4s2nUuaUohK4Nj0A2ZVrQmSZ65x/AI3nO2GI5GZWdouChppSzGUzK6K0mGAlIqIZRUAgJuuIKfrIiyRAkwxosoGouJ5sdVhhiapMtRIRERFlASMWReSDd6H5WlPmrPMr4Vxbz2ZWwxhC4JPLPTjWmtrMyu204cGlZShgMyuiEfGnCRERzRgCAlFZR3y05OrNeyRAlQyoJbmQDAGrosHa0QaltPy2mxgMvxY9kibfjVPYY63n1WoiIiKiyaP39yJ8YBeM/tRkof2Ou2BfcRebWQ0T1wzsa/bjcnc4ZW6e24X7akpgt7CZFdFomGAlIqLxM7GUgoBARNGgykbKnKMnDFk3oJbKUCUDIx1TFbKEuKwj/u5bkF05sC6ohnVBFeQiz7ifZNfvrE9Kno4kGL/R0G2s9fU765lkJSIiyhYsTTWlNF8rwu/vgYgnN7OCosC5ZjNsC6vNCSxDjdbM6o45BVizkM2siMaDCVYiIsp4AkBY0aDdnFwVgLM7BFso8YTQqlshIBInVmUD2ijJViMcQuz0McROH4OcVwDrwmpYF1ZDyXdP7QdDRERERFMi3nwakU8+SG1m5XDBtekBWDxlJkWWmUZrZrW+shhL2cyKaNyYYCUiohvqx7j6Prz52FhrJ+lEhpCAsCcvbXLVpVtgDSX/tV2CBJtQYNMVGBBQZQOqZGC0ogLGQB9iJ44gduIIlKKSRLJ1QeWYTQ9Wekdu1Db81OpI68ZzEpaIiIhowsZ63gaY8jxvsgjDQPQvHyN+9kTKnFJYDNemhyDnsJnVcGc7BvD+hfTNrO6vLcWis5omAAAgAElEQVScAjazIroV055gvXjxIp599lm43W688sorQ+PPPPMMAoHAuB5j2bJl+OEPfzgp8fh8Przxxhs4fvw4wuEw5s6di4cffhgbNmwYdV9LSwt+//vf4/Tp04jH41i0aBEeffRRrBytKzoRUSarr09+Yp1O8Ma19zHX1tdP+Mm3AYFwSR50e/KvK0kATt0Kq5BH3S9Dgt1QYIcCo60PqssGtUwaNdmqdwegdwcQPfoxLKVzYF1QBcv8RZDtjgl9LERERESmGM9zPGDan+dNFhGPIXxwL7S2Kylz1nkL4bx3CyQrmzMNYjMroqkxrQnWaDSKl19+GaqqpgZiscA6xg89XddhGMakFaO+ePEifvjDHyISiQBIHIO/dOkSfvazn6GjowNf/vKX0+5ramrCT37yE+i6PrTv7NmzeOGFF/DUU09h8+bNkxIfEVE2MyJhhC1q2uSqS7fCMkZy9WaybsA+EIVds0H/67+Feuk81MsXYAT7028QAlpHK7SOVuDTg7CUz0uUENBF+vVERERENK2MgX6EDrwDo68nZc6+/E7Y6+5mM6th4pqBvef8uNKT2syqotCFLUvYzIrodk1rgvWXv/wl2tvb08799Kc/HXWvYRj4x3/8R1y5cgWbNm2acCyhUAgvvPACIpEIysrK8Mwzz2DJkiVoaWnBSy+9hDfeeAN1dXVYvHhx0j6fz4cXX3wRuq6jsrISTz31FCoqKnDixAm8/PLL2L59O5YtW4bS0tIJx0hEZJqRTuMPP80wnjW3yQgOILT3zzCk5GSmJIAczQoFt5ZcvZniLoKycjXsdXdD7/IPJVtFNDJCQDq01svQWi/jC2fjcBs5aM6JQzIEhMwn7RNRv/PGFUQ2+iIiIppio924nKbneZNF87cj3LgbIhZNnpAVOO/ZCFvlEnMCy1D915tZ9aRpZvVXcwpwD5tZEU3ItCVY9+/fj8bGRkiSBCFu/fTPe++9hytXrqCqqmpSEqx/+MMf0NvbC7vdjmeffRZlZYli19XV1fjOd76Df/qnf8KOHTvwz//8z0n7fvOb3yAWi6GwsBDPPvsscnMTdVzq6urwzW9+E//+7/+OX//619i2bduEYyQiykZ6Xy/Ce/8MIxxMGpevtcEVGICi3VSL9Vaus91EkiRYPGWweMrgWLUWekcb4pfOQ7t6EUJNffIJABYDWBK0YUnQhsJYHG35Cq4WyOhySUCmPyk1oWvx8ARqOsPr0I61droSsEz6EhERZbb4hTOIHP4AMG5qzuRwwrXhAVhKvSZFlpna+yLYc8aPqJb8+ZIlCeurPKgtyzMpMqLZY1oSrG1tbdi+fTskScIXv/hFvPXWW7e0PxwO43e/+x0kScI3v/nNCR/x13Ud7733HgCgoaFhKLk6aOnSpaiurkZzczM6Ozvh8XgAAP39/Th06BAA4JFHHhlKrg5at24dXnvtNRw5cgSqqo5Z8oCIiJLp3Z0I7fuflJOksmogJzAAWTdG2DlxkizDUj4PlvJ5EKvXQ2u9CvXyeaitlwE9fdVWuwYs6taxqFtH2CrhWoGMawUy+hwZnmidJvU768ds5BWM30iQj7W2fmc9E55ERERZTBgGYk2HETt9LGVOLihCzuaHIOcyWTjcSM2sHNebWZWzmRXRpJjyBKuqqnjppZcQi8XwN3/zN7jzzjtvOcH6X//1X+jr68OGDRuwZMnEj/lfvXoVoVAIALB27dq0a1auXInz58+jqakJDQ0NAIAzZ87AMIwR9ymKghUrVuDAgQM4ffo0VqxYMeFYiYiyhRbwIbzvHQg1dmPw8cch7/j/kKNZIXtHSFqO5zrbcOM4uSkpFljnL4J1/iKIeAzq1UtQL5+H5msdcY9LFVjSqWNJp45+uwRFd+CiS0W3dbSWWkREREQ0HkKNJ5pZtV5OmbPMXQDXui2QrDYTIstMhhA4dKkbJ9r6UubcThseWlaGfAcPhRFNlilPsO7cuROXL19GTU0Nvva1r+HMmTO3tL+vrw/vvPMOZFnG3/7t305KTIN1YC0WC6qqqtKuWbBgAYDE6dtBPp8PAFBaWorCwsJR97W2tt5ygrWrq2vMNW63G4rCotNENLto7dcQPrALQteSxpXiUrjeeg+ywzHy5im+9i7Z7LBV1cBWVQMjEsYx368grmjwxkb+FZofE1gddGJ1rxMxWcAeV9HlktDlktHjlGBkYd3Wld70ye/hp1bHs4aIiIiyjxEcQPjALui9qa+Z7UvrYF+5GpI8sRr9s0lcM/DeOT+ujtDM6r4lpbBZ+PkimkxTmmA9fPgwdu/ejby8PGzbtu22EoN//vOfEY/Hce+998LrnZw6KgMDAwASidKRYiooKAAA+P3+lH3l5eUjPnZ+fn7KvvF66qmnxlzz6quvori4+JYfm4goU6lXLiJ88L2UGlqW0jlwbX4wo04iyE4XWooVNKkDyFNlfN4xFxV9BvKjI9cWtxsSvAMGvAMAoMOQgB5nItnaG7bCZ9dG3EtEREQzUP3oNcWTbt+MtRaYtrrpmUoL+BLNrG5uRiorcK5eD1tVrTmBZajRmlmtmFOA1WxmRTQlpizB2tnZiVdffRWSJOGZZ565raRgOBzG7t27AQBf+tKXJi02TUu8mM3JyRlxzeBcT0/P0JiqqmPuG6zLOnwfERGlF285h8jHBwCRXFvVMncBXOsbIFmmrRfjLRuwGjhXYsG5EiA/aqCiz8C8PgOu+OiNHGUBFIcFisM68oOJ3xmucByRQ41QSrywlHgh5eZNuN44zUImNCkjIqJbVF8/dtPNW23QWV+ftT/34y3nEDnUmNrMyu6Aa8P9sJTNMSmyzNTWF8G7IzSz2lDlQQ2bWRFNmSl55arrOn76058iFArhkUcewapVq27rcXbt2oVwOIw777wTCxcunLT45OtXB2y2kU9FWa6/qI/Hb/zV53b3jderr7465hq3233Lj0tElIliZ08i+unBlHHrgio419ZDmkHlUPodMk45ZJwqFSiKCMzrM2BEDeRp47t6lR8TiJ8/DZw/DQCQnDmwlHihlJTBUuKFXFjMa29ERESUNYQQiB3/FLGTf0mZkwsK4dr0IJS8AhMiy1xnrjezEmxmRWSKKUmwvv766zh79ixqamrw1a9+9bYewzCModOr999//2SGN5QgHa1kweDJoeGJUrvdflv7xotX/4koGwghED/VhOixwylztuqlcNy9fuYmEyUJ3S4J3S4ZTVIfcjQJ5TEr7nUpKA4ZKIgJSKMfcAUAiEgI6pULUK9cSDysxQrFUzp0wlXxlEGysilBNtEMA/02F6yGjlwtBp5vJiKaAUZqvjneBp3jOd06CwnDQPTTg4g3f5YyZymvgGv9fZBsdhMiy0yaYeDw5R6cTNPMqtBlw4NL2cyKaDpMeoL1xIkT+NOf/jShuqsAcPz4cXR1dSEvLw8rx9MV+haM5xp/KBQCgKS//tzuPiIiShBCINZ0CLHPjqXM2ZfWwX7nPbPqanzIInDeEkeuN/Hr1qoLFIYFiiMGisMCWljAYoz98QpNheZrheZrRQwAJBlKYXEi4VqaSLjKrpHL19DMJIRAe38UzYEgLnaFEF/wOQBAjhZD+Vk/yvIdKM93oNBlnVXfN0RElL2EriPy0T6oly+kzNlq7oBj1dqZ+4f4KXCtN4KDLZ3oi6gpc/MLXdjCZlZE02bSE6yNjY0QQmBgYGDUpk2BQABf+cpXAABPP/00Nm/enDS/f/9+AMC6deuGrt1PFo/HA2D0RlS9vb0AAMewztW3u4+IiEY/jWCvuxv25XfO+iSRqkjw50nw5yWe6B6398ITV7AxZx6sFYugBXypDRzSEQb07gD07gDiZ08AAOTc/BsnXEu9kPPd0/r5FEIAug6hxiHicQg1htKggaqQFTZDwmKLBosOWA3ApgtYDMCqA0t68qFLAgGbjgKHjm6njJANwCz/WhhNdyiO5kAQFzqDCMZSm6CFLHac7wzifGeihp/dosCbb4c3zwFvgQOeHDsUOXs/f0RENDMJTUP4/T3Q2q4kT0gSHJ9bB/uS5eYEloFCcQ0fX+zGhc5g2vm6uW7cvaCQzayIptGkJ1gVRYF1lGuLhmFA1xMFlwfXyTf9BSoYDOKTTz4BAGzcuHGyQ0RFRQVsNhtisRhaW1sxd+7clDXNzc0Akq/tV1VVAQDa29sRiUTgdKbWMDl37lzKPiKibCd0HZGP90O9dD5lznHXOthr7zAhKvMZEuC36zjvUeDa+ACEEDAG+qAHfND9PmgBH4yB1OteaR8r2A8j2A/1YuL3kGR3QPGUJU64lnihFHlGvVYuDAMiHgfU+PUkaex6onTYf6sqEI8NG4snvX1zA4p1l1RUXW/k5Y3p6d4ttHjipktpzALvtUQyMWaR0OW6Xm7BKUMRgD7LXx+EGh7E+bxSnM8vRZc9N/0in+/G2zt3Dr0ZA3D5+j8AYDEMlG57Gt58B7z5DpTl2WFVbu/0Sv3O0btbN/luXF8da+2+x7OzQQsREY1NxGMIHdgF3d+ePCErcK3bAuv8SnMCyzCGEDjV3o9Pr/RA1Y2UeTazIjLPpCdYt27diq1bt444f+rUKfzoRz9CSUkJXnnllbRrjhw5AlVV4Xa7UV1dPdkhwmq1YunSpTh27BgOHjw4dJJ2uBMnEieCFi1aNDTm8XhQXl6O9vZ2HDp0KOXUrWEYOHXqFACgspK/AIiIAEDoGsLvvwut9XLyhCTBec8m2KpqzAksA0mSBCXfDSXfDVTVAgCMaAR6oAN6IJFw1bsCgEh9Qn0zEYtCa7184/OuKFAUFTIAAUDs+uONxKgah9BSr5ZNJl/Ql3Y8rsfTrpF7AQ8S/1RJhQjYNORAg3rlIpSSMshO15TGOx3imoFL3SGc/9//B63OBRC6BPQEAaQ/jYLh9d196T+fAKABaPt/f462xx8HkPi68uTY4L1eUsCb74DDOnYJp/qd9UkJ1HSC8RuxjrW2fmc9k6xERJTCiEYQ3vtn6D1dSeOSYoFr04OwlM8zKbLM4uuP4oMLnegOp+/3UprnwPrKYnhyWZ+WyAxT0uRqoo4ePQoAWL586q4AbNq0CceOHcPbb7+NhoYGFBUVDc2dPHly6ATrqlWrUvb99re/xZtvvok1a9YklQLYv38/enp6IMsy6urqpix2IqKZQqhxhA/shtbRmjwhK3Ctuw/W+YvSb6QhssMJuWIhrBULASSuz+ld/kSy1e+D3tkBoY6jsaKuQ5cNDJ0j7eyYqpAnnSIAb8wCb6eO8PuJBphybj4UTxmUkjJYPGWQ3UUzoiabbgi09kVw3h/Epe4wNMMAcgqBgZETpoWhPiz2X0b1xc+gWqzwFZSgvXwBfPklCDrGTjQLIRAIxhAIxnDiegMMt3N4wtWOPDa/ICIiExihIELv/XfKjR3Jaoer/iFYSrwmRZY5oqqOQ5e7cbZjIO283aJg9YJC1JblzfpyW0SZLOMSrIZh4NixRPOT8SRYA4EAtm3bBgB48sknx11SYM2aNXj99dfR0dGB5557Dlu3bkVlZSWamprw85//HABQW1ubcoK2oaEBb731Fvx+P55//nl8+9vfRllZGT788ENs374dQKJubGFh4bg/ZiKi2ciAQHjv/0C/KZHH0wgTI1kssJTNgaVsDoDE1X6jtzuRcO3sgO5vhxEOmRwlAEXB3615Cj87+nNoMhBXJKgKoMmJf6sKoMoSTvachVOX4I1ZcJetHHZt5CaRj698fOjtoZIIlxJ/EJWsNijFJTfq0BaXZEyH4USCM1FXtaUziIiavlwCAMCbeCHp0uKoHvCjur8DxUYIkscBXJMB6Cjs92FppRdQryEo7Gh3FsDnLEC7swC93f3jiqk3EkdvJI4zHYn1uXbLUA1Xb16icdZwK73pG44OP7U6njVERESD9L5ehPf+d8rzFsnhQs6WL0ApzO6ye0IInPUHcehSN2Ja+ucONaV5WL2gCE7b7TUXJ6LJk3EJ1nPnziEUSvyAveOOsWvyCSGgqolrjYYx9pXJQRaLBdu2bcOPf/xjtLW14Qc/+EHSfE5OTtpSB/n5+fjud7+LF198Ec3Nzfje976XNF9SUoLHHnts3HEQEc1GhiwhbFFTk6tWG1ybPw9LKU8jTBZJlqEUeaAUeYCaxO9NIxS8ccI14IPe1w2IkROXaR4Uks0GyWq7/m87JKsVsNkTY4Pjg2ustkQy02qFNLhGSTzR/z+PfmPUdzVYt7MDwD88thdGsD9REqGzI/HvccYu1Dg0Xys0XytiACBJUAqKoJSUDZ10lXPzp/VkR39URXMgiPOBYNruvjez6hoW9Xegur8DcyK9GM953FwthsUDfiweSDTgjJ78DL6CEvjs96A9147OUDzRgGwMwZiG87HkxllqdDkciEBFAEJIkKRb+BoiIiIahd4VQGjf/0DEoknjcm4+XFu+ACWvwKTIMkNnMIYPWrrgH4imnS9y2bC+ygNvPptrE2WKaU+wLl++HL/73e9GnK+trR11/malpaW3tH64qqoqvPDCC9ixYweOHj069AJk+fLl+Na3voU5c+ak3bdq1So8//zz2LFjB86cOQMgUd/s7rvvxhNPPIGCguz+ZUBE2c1QZIRK8mDclIyR7A7kbPnrRCKQppSckwtbTjWwMHELQ8Rj0AId0P/jVwAACYC0egNwPTEqDU+MWm2AxWLKFTNJkqDkFSReVFUuScSuxqF3+m+c0O30j68kghDQe7ug93YBzZ8lHt/hhOV6slXxeKEUeyApk/tUKKrquNAZwvlAEB0jvCgaTpIkVIS6UX3mIyzsaoNlxV9N6P07tDgWdrViYWcLUDcXqm6gYyCGjv4o2vuj8A/EEmUJxhDTdBhaMVxSouSRGi6FJPdDUvogK32Jt6Xx/2GbiIhokOZvR3j/Oym/z+WCQuRs+WvIrhyTIjNfXDPw6dUenGrrh0DqHzatioy7KgqxvDwfisxyAESZJONOsE43r9eL73//++jt7YXf70dxcTGKi8e+ilBZWYnnnnsOnZ2d6O7uRllZGROrRJT1dBgIl+bBsCSfvZNdOXBt+SKUArdJkWU3yWaHde58WI1hv/YXLzMvoFsgWW2wlM8bKikhDANGXw/0zg5o12vQGsHxXYsX0QjUa5fwy//+EQDAkIAep4Rul4xul4Qup4yYVUq60j54wnY0e77xHq50R3A+EMSVnjCMcZwYLc1zYHFJLiqLc+BsOwkEro7rY7hVVkXGPLcT89xOAIkasJ2hGHz9Mfj6I/D1x0a8djicEDKE7gZ0N3QAkiQgyUE4IaDBDxUzp6YvERGZR229jPD7ewA9+XePUlwK1+bPQ3Zk54lMIQQudIbw8aVuhONa2jWVnlysWViEXHvWp3GIMhK/M69zu91wu2/9hb/H44HHw9NYREQ6DIQsGsTNydW8gsRphNw8kyKj2USSZSiFxVAKi2G7niQ2IuGhsgJawAe9uxMw0icNdzbthC84rKHUAJCHxD8LAPRbDeQoCtptcbTZVBxrb4IY4YCIBSWwYQE2/fz/weN1T4wZe77DisUluagqyYXbaU5TKUWWUJbnQFmeA3VzCyCEQE9YhW8gCl9fFL6BKIKx9C/shhNCgtDz4JRqANTAEFFocRmKxQdJHvvkLhERZZ/4pfOIfLgPEMk3ICxlc+Ha9EDiFk0W6g3HcbClC619kbTz+Q4r1lcWY17h2I0ticg8TLASEdGEaZKBsKKmJKLkgiLkbPlCVl/1oqknO12Q5y+Cdf4iAIDQNejdnUlJVxFN/6LlZvmqjNqoA7WhxAkayWqF36ajw66hw64hYHfBUBbAhvlQpMQLHSFGfjrlsCio9OSguiQXZXn2jOvuK0kSinJsKMqxYZk3HwAwEFXh64/hP0+1QxcDUKT8MR9HlhzQ417o8fmQlV7I1nbISifrthIREQAgdu4Uop8eTKmrbp23EM719016yZ6ZQNMNHL3Wi2OtfWlvvyiyhJXz3KibWwCLPJ7K7ERkpuz7KUZERJNqpOSqYkhw3f8wZHt2XvUi80iKBZYSLywliWZqQoih5lkXr/wK4biOIlVBeU76ZmvDT7h6XV54JRt6pDL0Gl5E4nmIK0DEKuFavBshxcDNOURFlrCgKAfVJTmocLtmXI20PIcVeQ4rLPZm9KEJkrBhnmMDDL0AwiiAMHIhRjrWC8DQ3TB0NyRJhWzpgIzzMBCcxo+AiIgyhRACsVNHETv2ScqctbIGzns2QsrC5OHl7jAOtnQhGEvfBLOi0IV7FxWjwKQbL0R065hgJSKi26ZKOsKKluiaNIwlqsGlWyExuUoZYHjzrKY5FjTJ/bAZEjbnV6AobKAoLFAUMWAdVlXAgAUhWzlCjuUIyoXAsJOnNh2w6QJKPPGix6XqUFvOYk5RHhbP86Cqch6cebnT/WFOGYE4ZEsXZEtX4r+FDGHkw9ALoAWDsEjpa9cLYYWuzoNb+gJUEYCuWaEZBk/hEBFlCSEEon/5GPEzx1PmbDV/BcddazPuZsdUG4iq+OhiNy51h9LO59gsuLeyGAuLXFn3uSGa6ZhgJSKi2xKXdETSJVcjKlxdQUhz+aSQMldcFvDnyvDnXk/2GQI5URdyIwXo6y6HQ5TDJizIVUZPlNr0fsyN+fAlLEBurwH0AvGTgJaTB8VTCsXjTfy7sBiSokzDRzb1JMmApPRCVnrRjyYoIh92VEKSPCOWS7BKJdCiXrz2yVUsLs1FbWkeinKys9YeEVE2EIaB6KFGxFvOpszZV3wO9jtWZVUCUTcETrT14S9Xe6EZRsq8JElYMacAqyrcsCr8QyTRTMQEKxERAOzbZ9q7Ht6xfLhgPDjmGrPEcu2IWlIb4VgNGc7O4M05V5ou9WN0vG8a9nU01loTvyemi2G4IK5fZxd6AWLCim470JHvA6DCYmhYYMuHUxNwqoBTE5AFYDVicOsdcPcfh13vR2muF7ly8oslIzQAIzQA9fKFxICiQCkqgcVTdj3xWgrZNTtOueroRxhNsLokGHoJDNULQ0/fODSm6TjZ1oeTbX0oy3OgtiwPVZ4cWPhikoho1hC6hsjBvVCvXkyZc9y1DvbaO0yIyjxtfRF8cKELvZF42nlvvgPrKz38wyPRDMcEKxERjZsAEMt3IFbgTJmz6QochsLkqlnq65MTqOkEh9XBHGttff2sSrIKIdAXUaGr5ciBE1aUQg0vGHWPJgsE7RKCdgmSpENWAijQfSiJ9kKL6IgM9MCuj/NUqq5DD/igB27Ud5VduVA8ZVBkHYpNgRLXZ/T3jyQJKBY/FIsfwnBCV8thaGUjru8YiKJjIIqPLnWh2pOL2rI8eHLt0xgxERFNNqGqCDfuguZrTZ6QZDjXboZt0WJzAjNBOK7h40vdOB9IX4fcaVVwz8IiLC7JzarTvESzFROsREQm2Pf4PtTvHP0E4fBTqyu9K8d8vKkmIBArcCKWn1pX1a4rsBsKJDPSQ7MoCUiTRwiBvqiG9r4I2vqiaOuLIqJq0GKLYZd8Yz/A9ceQLd2JRk1KFyTJQAhAKEfGJchosvTDoSdquX77jlXQOzugd/ohtPQNK25mhIMwrgShKhpQlg8IQFHiUIQMi5CgCBkSYM731QRJcgQWewuE7SKCA52woxJA+qZicc3AZ75+fObrR0muHUu9+ajy5PCKJBHRDGPEogjvfwd6Z0fyhKLAtb4B1nkLTYlruhlC4LRv4P9n706D7LrO897/157O2POMRqMb8ww0wQkkRYqgKcXXli3XlSuV1HVZGSqxaDmxvjjxragcx46vyuVyyalEpbhiVcRbtuUhjuzilV2WRYGmzAEkQWKege5GY+h5OvOe1v2wG0A3+vQAoGe8vyqwgbPXPmeBAE7v8+y13pcPekbwgjLlAFDsbq7gqU01xO31UT5ICCEBqxBCrJj5QtGpAexyBKhz0WgKpo9XJlyNBybOSoWrorzOWQL5qatWFzJmDdFaM1H0uT1R5NZkqJp3Z5axmI8yipR0Fx4DePTRHt895/iiqemrMIgffDqaRxgSjo8SDA0QDPXjD/UTTowt8MUhMDQBAXc2ESqt7oatpmNhej5KP/Bva8UopXHpxaUXJ1niYOsvc2kgS8Er/2czmC0xeGWQ97qG2VqfZndzBfUpR1b2CCHEKhfmc+R++NeE4yPTHleWTfLlH8dq2rBCM1teA5kS/3B1iKFcqezx+nSMT22pp7FCdmwIsd5IwCqEEGJOvgopmD5hmVQnHljEQrnzLlZGpujdXZ16e6JAtvQwgWoJwxxDGWOTX0vkJh4+ZFaGgVlTh1lTB9ujcDYsFQmGBwkG+wmG+wmGBtFe+Q9e99NK4ymNRwhNFdFrhBrDcjG0wtAKhZr8ORiKVRvAKqPIsx21PLWphuujec73Zbg5VkAzc8JeEHKhf4IL/RPUp2LsbKpgW0OKmCXvN0IIsdqEmQlyP/weYXZi2uMqFid15Ccw6xpWaGbLp+gFfHh9lAt9mbLf1xzL4OlNtexursCQm4ZCrEsSsAohhChLo3GNkKLhM2NxqoaEb+FoCTvE8smW/LurU2+NF8mWFrYVfypllCjpHnwGcJJNoIos9eccIxbH2NCGvaENiFbbhhNjUeA61I+vFaFm5r+zWWhDEShNUC5J3ViDCjSG6WL86AcY6TQqVYGRqsBIR1+VtbKXf6ah2FyXYnNdikzR4+JAlov9GXKzrDgeypUYulbiWPcIW+tT7GquoDEdk1WtQgixCgRjI+R++NfoQm7a40YyRfKVn8SsqlmhmS0PrTWXBrIc6x6h6Adlx2xvqODZjhqSjsQvQqxn8i9cCCHEDOFkSQDfKFM3KtQkhnPY9RKuiqWVLfncHi9yayIKVTPFBw9UE7ZFa1Wclqo4LVUJ/vjysbsrVJVRtdhTXhClFGZVTfShc9su8B30zTECx8RvaJ0MT0P0Q+aH2lRRqYHrV8u/fjw5Gbam7wWv6Tq0aX8AACAASURBVEpUKo2RSqPM5bs8rIjbPLWphkNt1VwfzXOxP8P1kfKrWv0w5OJAhosDGWqSDrubKtjWkJb6dUIIsUL8oX7yR/8G7U7flWFUVJF65Scx0hUrNLPlMZxzeefaEH0TxbLHqxMOn9pax4YyzWGFEOuPBKxCCCGm8WMWectDl1kdZ5Z8ksM5jDIF+4V4VDl3MlCd3PI/XniYQNWkpSrBhqo4GyrjVCXsaSsdV+uiR6U1VsnHCqNLM40mJFqlGhhR4Bo8wCrXuehinqCYn9mE5M5cEimMVJqnbng4mQQZK6AhHZK3FXk7WkG72Ayl6KhN0VGbIlvyoyC1PzNr2YfRvMu7XcMc6xlhS12aXU1pmivjsqpVCCHmM1et9Wx2YeOISkjl3/zejMaOZk0dySM/gZFIPsosVzUvCDl+fZTTtyfQeub1smUYPNlWzb4NVZhL8D1TCLE6ScAqhBACiAKdUlWCUkV8ZhFHDbHQJDaQkVZWYtG4fkjo1xMGNYRBFX/04fUHfo64ZdJSFWfDZKhafV+gulYpFCYKUwOTOw71jVFCy0CbBmH9RkKlox9oQkXZmyIPQxdyBIUcbWMhdjZqbNc8WY5BKyhYirwTha12Ns6QE9CTePAwfDbpmMWTbTU8sbGam2MFzvdn6BnJl/0QG4Say4MZLg9mqE447GqqYEejrGoVQoil5CVs8qYH94erDc2kXv5xlLM+Gzhprbk6lONY98isZW066lI8v7mOdEyiFiEeN/KvXgghBGE2Q970CCrjM44prUgGFpY2VmBmYr3Jlnx6RvL0jOS5NV7AK+55oPNjdwLVymjLf21yfQSqC6EA0w/BD0Gb3L+LXqMJb48TWiZhg4Xec5AwmyXMZQhzGXSx8Ohz0JD0NEkvenEnG217HLMDxlIBtyuMRVsmbChFW02Stpokedfn0kCW8/2ZWUtFjBVc3u8e5oOeETbXpdhYnaCpMk5V3Hps/o4IIURZR4/CkSPzj5u6arWzs+wQVwUUTB/+2RenPW61tJF88TMo236Uma5aQ9kS73YNz1oOoCJu8/zmOtpr1+/KXSHE3CRgFUKIx5x3/RqF999GG2W2OIUGicDCkHWr4iFprRnOufSM5ukZzjOUK81/0hSOZdBSGa1QbamMU5dyJCybhUJh+mEUwoYmPHF42nHteXfD1jCbQecmw9fsRPRr98H+bKaq9kx2XfcZShmcbjYZSyzuDZmkY9G5sZqDrVXcHC9yoT9D93COsMyq1lBrrg5luToUbXVN2CZNFXGaKmI0VcapTztYhtwwEkI8Zo4enX/M1BC2zPjS+VMUP35vxuP2pq0knj+CMtff7oGCG/DB9REu9WfL1gc3lKJzYzWdrVVYpnxvEeJxJgGrEEI8prTvU/z4PdzL58ochHho4YQGSsJV8YCCUHN7vBCFqiP5WetolmObUaDaUpWgtSpObcrBkEB1USjbxqyuxayuLXtcey5h9k4Am+VK7x8yGHpU+AZ1JtjlmyNPU58LOXI1pLfa4GyjRcFZ3D87pRQbqxNsrE5QcAMuDWa50D8xZ73eghfQPZKjeyTqcG0oRX06FgWuFXGaKmOkpLOzEELMSmtN6dRxSmeOzzjmbNtN/OlPodbZjasg1Jy9PcHx3lG8WXoPbKxO8PyWeqoT63PVrhDiwcjVpBBCPIaCsRHy//Am4fjIjGOGF5IMbEwpCSAeQMkP6B0t0DOS5/poftYPI/dTSqOMMQxzlJ850Ep9WgLVlaJsB7OmDrOmDoDTLRYnVLQKtLN5C3agSbqalKdJulGpgLwb0ODOXLHUNhayYcLlSp3J2RC8JXg7STgmB1urOLChkr6JIuf7M3QN5wjCuWvRhlozkCkykClymnEg2tp5N3CtiEmwL4QQk7TWFD96B/fS2RnHYns6iXU+s+52llwfzfNe1/CsN+8q4jaHO2rpqE2uu9+7EOLhScAqhBCPEa013pULFI+/iw5mriq0cy6J0RyqWcJVMb9M0aNnJE/3SJ7bE8WyTYjKcSyDTTVJ2muT/PHld1EqWhrZWLE+m2KsF56pGE8oxhP3HjvBBNvzDs+OJmi+b7wZws7BgP+rUM0H1QVU0+I04bqfUoqWqgQtVQmKXsDVoRw3xwv0T5QoeAtbPZ0pemSKHlcGo0DZNg0a01FJgaaKGI0VMWLW+tv6KoQQc9FBQOH9v8frvjzjWLzzWWJ7y9dpXavG8i7vdY/QO5ove9w2DTo3VrN/Q6WUmhFCzCABqxBCPCa0W6Jw7Ed416/OOKZMi7hvYY/kpCDAejC1ScVU2ez8Y+agtWYo59I9nKNnJM9I3l3wuemYTUddkvaaJM2VcUwj+pt2J1wVa5SCyymXa0mXL1ht7BgKZpQSSASKTw8nSSoP71Yv9oa2JZtO3DbZ21LJ3pZKtNZkSj79EyX6M0X6MyVGcm7ZGnr384KQm+MFbo5HjcEUiuqkTfNk4NpUEadSmmcJIdYx7fvk/+EH+Dd7ph9QisTTL+Js370yE1sCJT/g494xztyemPVm8faGCp5uryEdkwhFCFGevDsIIcRjwB/qp/APbxLmMjOOmTV1JF54FfP3/3AFZiZWOz8MuTVepGckqqeadxdeT7UhHaO9NklHbYqapC1h1DoWKLjUYNFdY7J7IKBjNOD+vnmVRU3+6F9jtWwk/sThu6UIlopSisq4TWXcZntjGgDXDxmYDFvvhK4LKWeh0YzmXUbzLuf7osekeZYQYr3SaPJH/wZ/4Nb0A4ZJ4rmXcTq2rczEFlmoNZcGsnzYM0LBK3/DtyEd4/ktdTRVxJd5dkKItUYCViGEWMe01rjnTlI8+SHomSGCs3Mf8SeeRZny7WDNO3p0evffcqauWu2cfVtf0bC4/p3/Tc+Ffm6MFRZcT9U0FK1VCdprk2yqTa76xkEn+sqv4s262XnHiPJcS3Fyg8W1WoN9/QHNmZl/d/zbN8j2/QXOlp3EDj6NkUgu2/wcy2BjTZKNNdFrhlozmveisHVypetEcfaGWVNJ8ywhypinC71Y/UI0ecsjuC9cVaZF4qXPLukuhOV0e7zAe10jDOVKZY8nbItn2mvY0ZiWG8RCiAWRqz4hhFinwkKewntH8W/fmHFMOTESh1/GbutY/omJpTPfh9k5PviOFbzJVao5+idK6MuDC3rJuGWyqTaqp7qxOoFtygo+AZm4wXvtBg3ZkOpLAfX3N8LSGvfqBbyeqzh7DhLbfRBlLf9lqaEUdSmHupTDnskisnnXZyBTom8iWuE6mC0RLqC+sDTPEkKsdSGanOURqunvecqOkXz5x7Ea76+2vfZkSz7Huke4OpQte9xQiv0bqnhiYzWOJdc0QoiFk4BVCCHWIe9WL4X3jqKLhRnHzMYWks+/gpFKr8DMxGqhtaY/U7q79X+ssPB6qlUJm/aaJB11KRorYmsqMDr6xaMceX3ulb5TV612Ns/fwOPoF2WV1mwG0wY/aJlgZ87hZ4KZf0+071E69RHelfPEDjyNvWXHiq8USjoWHXUWHXUpICqTMZR175UVeITmWZZhUJdyqE871KdiNKRjVCftNfVvSAixPgUTY+TLhavxBKkjP4FZW79CM1scfhBy8tY4J2+M44fld+Z01KZ4tqOWqoS9zLMTQqwHErAKIcQ6ooOA0qkPKZ07OfOgUsT2HSK27xBqvjqBS9QkSawsXxncSNbQk6rj+ofXZ603dj+FoqkyRntNtFK1Ouks8UyX1nyB6NQAVsLTR6cVXEi7/F2DzS/vfxL33El0MD2gDPM5Cu+/hXvpDPEnDmM1t67QbGeyDIPmyjjNlXGg6pGaZ/lhOHlOcdrzR6FrjPqUI6GrEGLZ+f23yP/DD9D3hatGMk3yx34Ss7J6hWb26LTWXBvOcax7hGyp/M2x6oTD85tr75aPEUKIhyEBqxBCrBNhZoL8O28SDA/MOGYkUySefwWracMKzEystJ6RPBf6J7i55Xn8O+H6POGqZRhsrJ6sp1qTJOGYc44XYj6BqYgfeApn225KJz/E7boE9229D0aGyL35/2G1tkeNsKpW34f6xWyeBfOHrg2Tq10ldBVCLDatNe75UxRPfDCjVr9RWU3qx34SI7l2dzwNZUu82zVM30Sx7HHHMniqrYY9LZXy/iqEeGQSsAohxDrgdl+h+MGP0N7Mbd5WazuJwy9jxOfpfrqITZLuPp9YFUbzLj0jeZhn5XLCtthUm6CjNkVrVRxL6qmKJWAkU1EX6p37KH7yPn7fzRlj/Js9ZG/14mzfTWz/U/O/f62wxWyeBbOHrvVph7qUhK5CiEenPY/C+3+Pd/3qjGNmqEh+5vOr/r13NgU34MPro1zsz5TdXaBQ7G6u4KlNNcRtuYEshFgcErAKIcQqtZCtydr3KX70Du7VCzMPGibxJw7j7Ny78JqGj9AkSaxe7bVJPugZKXusOuHQURdt/W9Mx1a8/qV4fJi19SRf+Un8W70UP3mfcHx0+gAd4l46i9d1hdi+Tpyd+1Dm2rh0Ldc8q+gFDGZLDOXc6Gu2NOt21XL8MKRvojhtJZZtGpOvE4WuDekYVQkJXYUQcwsmxsi//XeE4zOvDazQIBlYqDUYrgah5lzfBMd7R3H98rsIWqoSPL+5jrrU2i53JIRYfdbGVaoQQogZgtFh8u+8OTOUAIyKKpKfenXNNyQQi6M6EW1nngCUhubCGO0ddbTXJqWRg1hRSins1k1YLRvxrl6geOqjGc35tFei+Mkx3EvniHU+g92+dU3eCIjbJm01Sdqm1PgruAFDudLd4PVBQ1cvmD10nVrTVUJXIcQdXm931Ai1zK6nWGASC00Ua+/9onc0z3tdI7M27UzHbJ7bXEtHbXJNfg8RQqx+ErAKIcQao7XGvXyO4sfvQTCzjqa9eQeJp19A2XJnXkSUUhxqq4a+i2zKDRMPfWitWulpCXGXMgyc7XuwO7ZROnuC0oVTM97fwlyGwjtv4l48Q/zQYayG5hWa7eJJOCZtzhyha9ZlKLe4oWtDKkZ92pHQVYjHjA5DSqePUzrz8Yxjyo6ReOEI9h/8yQrM7NGMFTze7xrm+mi+7HHLMOjcWMWBDVVS+kgIsaQkYBVikUjXabEcwlKR4rG38Xq7ZhxTlk386U/hbNmxAjMTq92OxgrI9K/0NISYk7Id4p3P4GzfQ/HEB3jdl2eMCYb6yX3/r7A3bSXe+QxGReUKzHTpzBa6DuaisgJD2ajEQM5dnNC1qSJOR52UCBELMF+ddpheq32+8VJqaNmExSKFd3+If7t3xjGzuo7ES5/BrFhbN15dP+Tj3lHO3J4g1DPrrAJsa0jzTHst6ZjEHkKIpSfvNEIIsUb4A30U3nmTMJ+dccysrSfxwo9hVi5xx235MLS2yZ+fWCOMVJrkC6/gTzbCCgZuzxjjXb+Kd6Ob2M59OHs7MWJrr17gQiUck01Okk1lQtfBbInhRwxdT94cI2FbtNdG9Zhbq+NY8zTFE4+ZI0emh6ezyU65Rplv/JEj8n1pGQQjQ+Tf/j5hLjPjmN2xjcQzL6HstVMuSGvNpYEsH/SMUPBm7uQCqE/HeH5zHc2V6/f7ghBi9ZGAVQghVjkdhpTOnaB06jjomQX7nZ37iT/xzJpp/iKEEAtl1TeSevWn8Hu7KZ44RpgZnz4gDCidP4l79QKx/U/hbN+NMh+PjtDlQte8609rojWUdRccuhY8nwv9E1zon8A2DTZWJ2ivjZ5fumwLsTa5Vy9S+PBHM0tKKYP4oecerBHqKtA3UeTdrmGGsqWyxxO2xTPtNexoTK+p35cQYn2QT+NCCLGKhfkchXeP4vffnHFMxeIknnsZu7V9BWYmhBDLQymFvWkzVusm3MvnKJ0+jnanf7jWboni8XdwL50h/sRhrI3tj+WH66RjscmxZoSug5O1XIeyJQazLvl5QlcvCOkaztE1nEMpRXNlnI7J1a2V8bWz0k0skc7O2Y9NXbU627iFrIQVj0QHAcXj7+FePjvjmIonSb74GazGtVPHOlvy+aB7hCtDM3dxARhKsX9DFU9srMaxZPW9EGJlSMAqhBCrlHfzetTltVScccxq3EDihSMYyfQKzEwIIZafMk1iu/Zjb95O6cwnuJfOQnhfI6zMOPm3/xarcQOxJ57Fqm9codmuHknHor022v5/x53QtW+iSM9Iftau2xBtx709XuD2eIH3uoapTTp01KVor01Sn3IeyyBbiNUszGfJ/+gHBEMz666b9U0kX/wMRjK1AjN7cH4QcurWOCdujOOHM3dxAbTXJnm2o47qhNz8EUKsLAlYhRBilQkLeUqnj+NePjfzoFLE9j9JbO8TKKmPJ4R4DBmxOIknn8PZsYfSiQ/wrl+bMcYfuIX/t9/Fam0nfuApzNr6FZjp6jU1dH22o5axgkfPSJ6ekRz9EyU05RvGAIzkXUbyLh/3jpJyoufoqE3SUpXANCRsFWIl+f23yP/DD9DFwoxjzo69xA89tybKqGituTqU44OeEbKl8ivuqxMOz22undYQUAghVpIErEIIsUpo36d04RTu2RNo35tx3EimSLzwY1iNLSswO1HOib7Ztzlm3eyCxgkhHo5ZUUXyxc/gD/RR/Pg9guGBGWP8mz1kb/Zgb9pK7MCTmFU1KzDT1a86YVPdWsXB1ioKbsD10TzdIzlujBUIwtnD1pzrc65vgnN9Ud3WTTVRGYG2mgQxa/WHOEKsF1pr3AunKX5ybGa9ftMk8cxLOFt2rMzkHtBApsR7XcP0Z2bu4AJwLIMn22rY01wpN3WEEKuKBKxi7Tly5N7PpfOoWAd0GOJ1XaZ06kPCfK7sGHtjB/HDn17XXbKFEOJhWI3NpP7Rz+D1XKX0yTHC/Mwafd71q3i917A7thPbfwizomoFZrr6HHn93jXV0S9G11QJx2RnUwU7myrwg5AbYwW6R/JcH8lT9Mt37IaobuvVoSxXh7IYSrGhKk57bVRKIB2TjxxCLBXteRSO/T1ez9UZx4x0JckXP7MmVvFnSz4f9IxwZbB8nVWFYldzBU+11ZBw5AaOEGL1kasdIYRYQf7tGxQ/fp9gbLj8ANMk/sRhnB1rq8vrenb0i0enhRKzmbpqtbN5joYg3As2hBAPRymF07ENu60D9+JZSudOzKxfrTVe1yW87is4W3cS23cIIyV1rOdimQYddSk66lKEWtM/WbO1eyTPRHHmTos7Qq25MVbgxliBd65BfTo2WUogRW3Slu9nQiySYGKM/Nt/Rzg+MuOY1dJG4oVXVv3NeS8IOXVznJM3Z6+z2lKV4LmOWurTsWWenRBCLNyyB6xdXV189atfpbq6mm984xtzjvU8j1/91V+lt7eXT33qU/zbf/tvF30+fX19/Pmf/zmnTp0in8/T2trKT/3UT/Hiiy/Oed61a9f4i7/4C86fP4/rumzevJkvfOELdM7VVVMIISYFo8MUPzmGf7u3/AClsDfvIH7gKQkAVqGFBKLlVoYJIZaWMi1iew7ibN9N6eIZ3HOn0F5p+iAd4l45j3vtEs62XcT2PrFmGr6sJEMpWqoStFQleLajltG8R89onu7hHIPZ0pznDmVLDGVLHL8+SkXcvlu3tbkyjiFhqxAPxbvRTeHdo2hvZpO62L4nie0/tKrr9WutuTyY5cOeUXJu+TqrlXGbwx21tNcm5caMEGLVW9aAtVgs8nu/93t43ux3vKf6zne+Q29vLzU1NfyLf/EvFn0+XV1d/Pqv/zqFQlQEXClFd3c3//W//lf6+/v52Z/92bLnnThxgt/+7d8mCIK75128eJGvfe1rvPbaa7z88suLPlchxPoQ5rOUTn6E23UJdPm6dlZzK/EnDq+J7VxCPCoJn+9zYo56vdnswsYJlO0Q33cIZ/se3AuncS+cnlnbOgxwL53FvXqB2I59OHsOYsQTKzPhNUYpRW3KoTbl8MTGanKuH61sHc5za7xAOMv3N4BM0ePMrXHO3BonZplsqknSUZdkY3UC21y9YZAQq4UOQ0qnj1M68/GMY8qOkXj+ZeyNHcs/sQfQN1Hk3a5hhma5OWObBofaatjbUoG1ikNiIYSYalkD1j/4gz/g9u3bCxp77tw5vve97wHwC7/wC6TTi7uCK5fL8bWvfY1CoUBTUxNf/vKX2bFjB9euXePrX/86f/7nf87BgwfZvn37tPP6+vr43d/9XYIgYMuWLbz22mu0tbVx+vRpfu/3fo9vfetb7Nmzh8bGxkWdrxBibdOeS+ncKdzzJ9FB+bv0RlUt8UOHsVo2yl16IYRYBEYsTvzg0zg79+OeP4F78ezM9+AgoHT+JO7lczi79uPs2r/qt9SuNinHYk9zJXuaK3H9kN6xPD0jea6P5nH98lt+AUp+wOXBDJcHM5iGorUqQXtt1Cgr6UglMyHuF5aKFN75YdkdUEZVLclPf3ZV15jOFD2O9YxybWj2Oqu7myt4UuqsCiHWoGW7cnnrrbd4++23UUqh57irDVAoFPjGN76B1ppXXnmFQ4cOLfp8vvvd7zI2NkYsFuOrX/0qTU1NAGzbto1f+qVf4j/+x//It7/9bX7rt35r2nnf+c53KJVK1NTU8NWvfvVu8Hvw4EH++T//5/y3//bf+OM//mO+8pWvLPqchRBrjw5DvKsXKJ46ji7my45RiRTxA09hb9mxqrdyCSGWyNGj0xs4zmbqqtX5ShJJE8hpjHg8qme9cz+lcydwL5+HcHrDJu17lM58jHvxLM6eA8R27kPZzgrNeO1yLIOt9Wm21qcJQs3tybqtPSM5sqXyNxgBglBzfTQKZX90FaoTDs2VcZorY7RUxknHLLn5KB5rwcgQ+R/9HWF2YsYxu2MbiWdeQtn2Csxsfq4fcuLmGKdvjROE5bOAjdUJDnfUUZuS910hxNq0LAHrrVu3+Na3voVSis997nO88cYbc47/n//zfzI4OEh9fT0///M/v+jzCYKAN998E4BXX331brh6x+7du9m2bRuXL19maGiI+vpom+7ExATHjh0D4POf//yMVbUvvPACf/RHf8Tx48fxPA97lX6DE0IsPa01/q3rFD85Rjg+WnaMsmycPQeJ7Tqwai+IhRDLZCGB6NQQVgLUh2IkUySeeoHY7oNRmHr1IujpKyy1V6J08kPcC6eJ7emMmgxaspryYZiGYmN1go3VCZ7fXMtwzr3bJGs4N3fd1rGCy1jB5UJ/9OuUY9FSGaepMk5LZZwaaZYlHiOuCih8/y8hmH5jCGUQP3QYZ+e+VfnvIdSaSwNRndWCV/4GS3XC4dmOWjbVJFbl70EIIRZqya8WPc/j61//OqVSiZ/+6Z/miSeemDNg/eijj3jrrbdQSvHaa6+RTCYXfU69vb3kcjkAnnvuubJjOjs7uXLlCidOnODVV18F4MKFC4STnQ3LnWeaJgcOHODv//7vOX/+PAcOHFj0uQshVr9geJDix+/jD9wqP0ApnK27iB14CiOx+O9xYnWQ2qJi0UmoumiMVJrEsy/h7DlI6fTHeN2XZ9TF1qUixU/ep3T+FLG9T+Bs34UyJWh9WEop6tMx6tMxntxUQ6bo3Q1bb08U593hlnN9rgxluTK5tThmmTRXxmiuiNNcFac+FcM0JJwR64sGitVJXMufEa6qeJLki69iNbaszOTmcWu8wHtdI7PeTIlZJk+2VbO7uVL+7Qoh1oUlv0p8/fXX6enpYefOnfzTf/pPuXDhwqxjM5kMv//7vw/Ali1buHr1KsePHycWi7F7924OHDiAsQjbZ+/UgbUsi61bt5Yd097eDkSrb+/o6+sDoLGxkZqamjnPu3nz5gMHrMPDw/OOqa6uxjSlHo0Qq1GYzVA8+WH0QX0WVms78c5nMKtrl3FmQgghyjErqkg+f4RgbyelU8fxrl+dMUYX8xSPv4N7/iSxfYeici5yLfbIKuI2+zZUsW9DFUUvoHe0QM9ont7RPF4we93WO0p+MFl6ICq/YxkGjRWxybICcZoqYtI0S6xpIZp8YwVBbOZHdrO+ieSLr2IkF7dPyWLcyBsveBzrGaF7OFf2uFKKPc2VPNlWTdyW91IhxPqxpAHrBx98wPe//30qKir4yle+Mm8w+Id/+IeMj48DUQh6+vRpTNPk6tWrfPe732Xr1q388i//Ms3NzY80r0wmA0RB6WxzqqqKioMPDAzMOK+lZfa7hJWVlTPOW6jXXntt3jHf/OY3qaure+DnFkIsHe2WKJ39hNLFMzO3bk0ya+uJP3EYq7l1mWcnhBBiPmZVDckXXyUY6aR0+jjeje4ZY8J8lsIHb1M6d4LY/iexO7at6rrZR16fv67vib57dX3nG7+Uq/Ljtsn2xjTbG6O6rcM5l9sTRfonivRNFCn65b+3TuWHIbfGC9waLwCTK2ZTUR3XlsnQVcIcseym1s6eKpudc4wfs8jXpdBlwlVnx17ih55bdTd6Sn7AJ71jnLk9QTjLivRNNUkOd9RSnZQ6q0KI9WfJAtahoSG++c1vopTiy1/+8ryhYFdXF2+99RYAzz77LF/+8peJx6MOrr7v85d/+Zf82Z/9Gb/2a7/Gb//2b8+6gnQhfD+q/5JKpWYdc+fY6Oi92ome58173p26rFPPE2vfWvqQIpaPDgLcy+cpnTmOLhXLjjGSaWKdz0QfxKWulBBCrGpmbT3JT/8j/KEBSqc+KtupO8xOUHjvKKWzn0RBa/vWVff+fuT1I9OuS2aTde+FPPONP/L6kWW5fjF/7BUagUaAo0fRWjNW8Lg9Gbb2TRTnbJZ1h9aawWyJwWyJ07eiBRzVCYeWqjjNFXFaqqLGWWL9m3pdvtqvwTXgpmMUq5Nw/9uKaZJ45iWcLTtWYmqzCrXmQn+G49dHKXjlb4bUJB2e66hlY42UxhJCrF9LclURBAH/5b/8F3K5HJ///Oc5dOjQvOe88cYbaK2pqKiYFq5CtJX/Z3/2Z+np6eHYsWO88cYbj9T86k6ZAceZ/c6ZNdnMLTwIoQAAIABJREFUwHXdRz5vob75zW/OO6a6uvqBn1c8mrX8IUUsDa01fm8XxRMfEGbGy45RthPV7Nu5T5qjCCHEGmPVN2K98hP4A32UTn5YtqZ2ODFG4Z03cc+eIHbgKayN7asuaF0PlFLUJB1qkg57mqOdYtmSz+3xIn2ZIrfHi4wVFnbdfadx1vm+qAt7OmbdLSnQUhmnOiGNs8QiOHp0elPCBdJoCqaPZ8wskWH84i+RfOmzmLX1izHDRXNjNM973SOM5sv/G4xbJk+117CrqQJD/m0JIda5JfnU/6d/+qdcvHiRnTt38k/+yT9Z0Dlnz54F4MUXX5wWrk719NNPc+zYMY4fP/5IAeudgHSukgV3Lq6mBqWxWOyhzlso2fovxOrnD/ZR/Ph9gqH+8gOUgbNjD7F9hzDiieWdnBBCiEVlNTZjvvo5gr6bFE99VPa9PxgbJv/232LWNhA7+DRWy8ZVFdJ1NnfOemzqDeHZxi3kJvNyS8esuyUFAIpecHd16+2JIkM5d96mWRAFtVcGs1wZjG6Sxy0zqt86GbjWpRxpvrMGzLdzbEV2mT1gLdMgM07h7b8jGJvZk8NqaSPxwisYsfKfkVfCWN7l/e4Rro/myx43lGJfSyVPtFUTs1ZXKQMhhFgqix6wnj59mr/6q79acN3VOyYmojvJ27Ztm3XMndWbQ0NDjzTHhWzjz+WiotxTL84e9jyxfqzHDyliYYLMOKUTH+BdvzbrGLttM7HOZzArZaW5EEKsF0oprJaNpJpb8W/1Ujr1IcHIzGvRYGSQ/NG/xmxoJn7waaymDSsw28dT3DbpqEvRUReV8fKCkP5Mif7JwHUgU8IP52+cVfQDukdydI9E1/O2adCYvtc4qy7lrK46rlNXSS5Cc6K1aCE7zVb7LjPvZg+Fd46ivdKMY7F9h4jtf3LV1HsuegHHe8c41zcx6+fdjroUz7bXUpWwl3l2QgixshY9YH377bfRWpPJZOZs2jQ4OMg//sf/GIBf/MVfJJVKMTExQTI5e12WfD66Q3ZnJenDqq+PtlbM1YhqbGwMYNpq2oc9TwixdoXFIqUzH+NePgfhLA2s6puiBlaNj9aATwghxOqllMJu3YS1oQ2/t5viqY8Ix0dmjAsG+8j94A2s5taodECDfG9YbrZpsLE6wcbqaCdJEGqGciX6Jkr0TRTomyhRWkDjLC8IuTle4OZk4yyAlGNRk3SoSznUJh1qkjbVSRtrlQRgYu3QWlM6dZzSmeMzjinbIfH8EeyNHcs/sTKCUHO+b4LjvWOz/tupS8U43FFLa7Xs4BJCPJ4WPWA1TRPbnv1uVRiGBJNdtu+MMwyDtrY2zp49y61bt2at2XrlyhUAWlsfrQt3W1sbjuNQKpW4efNm2ee7fPkyMH3b/tatWwG4ffs2hUKBRGLmN49Lly7NOE8IsfbowMe9eIbSmRNlVxQAGOlK4p3PYG3asqq2gwohhFg6SinsTZuxNrbjXb9G6dRHZetx+3038ftuYrW2Ez/w1Kqrnfg4MQ1FU0Wcpoo4B1ur0FozmvfoyxTpm6zlupDGWQA51yfn+twYu7c1WilFVdyOQtdUFLrWJR3SMUuuD5bRQnaQrZZdZmGpSOHdo/i3rs84ZlTVknzpM6tiR5TWmuujBY51j8xa6zhhWzzdXsOOxrTUWRVCPNYWPWD90pe+xJe+9KVZj589e5b/9J/+Ew0NDXzjG9+4+/jY2Bhnz57lb/7mb3j11VdnrAAdGRnhhz/8IQBPPvnkI83Rtm12797NyZMneeedd+6upJ3q9OnTAGzevPnuY/X19bS0tHD79m2OHTvGyy+/PO2cMAzv1pLdsmXLI81RCLEytNZ4PVcpnfiAMJcpO0Y5MWL7n8TZvhtlSgMrIYR4HCnDwOnYhr1pC17XJUqnPy77fcO/2UP2Zg9222bsrbswa2pRidSyBW9zBUcPsnV6PVFKUTsZht5pnJUpevRNlLg9Wct1oY2zILp2uNNA6+qU6hG2aVCbjF6nNulQm7SpTTlSk/Ixpj0Pt+sS7rmTZd8v7PatJJ79NGqOBUvLZSTn8n73MDfGCmWPm4Zi/4YqOlurcSxZwS2EEKsmGfjsZz/L9773PQYHB/na177Gz/3cz7F161Z83+fkyZO8/vrrZLNZKisr+exnP/vIr/fpT3+akydP3g10a2tr7x47c+bM3RWs96+m/fSnP82f/Mmf8L/+1//i8OHD04Lgt956i9HRUQzD4ODBg488RyHE8vL7b0UNrEYGyw8wTGI79+Hs7VxVjQaEEELcM7WJzXLUUlSGgbN1F3bHdrxrFyme/hhdyM0Y5/V24fV2RefYMYzqGszqusmvtRhVNfK9ZQVVxG0q4vbdxlkFN4hWuE4U+ZW//S10mEZrxT/r/OKCnzOqBVukP1Oc9njKsaaErlEAW52wpaHWOhZkxnEvncO7egHtlQnvlUH8iWdxdu1f8VXPBTfgeO8o5/syaMrXWd1Sn+bZ9hoq4isfBAshxGqxagLWeDzOv//3/57f+Z3f4fz58/yH//AfUEpNK57tOA7/7t/9u2l1WgcHB/nKV74CwC/8wi/w0ksvLej1Dh8+zJ/+6Z/S39/Pb/zGb/ClL32JLVu2cOLECf77f//vAOzatWtG061XX32VN954g4GBAX7zN3+Tf/2v/zVNTU28++67fOtb3wLghRdeoKam5pH+fwghlk8wPkrxk2P4N3tmHWN3bCN+4GmMisplnJkQQoi1QpkmzvY92Jt34F45T+nsJ+hi+ZVf2isRDPYRDPZNe9xIpjCqau8Grne+KuvBLtmPfvHovN3SYWFbp6c+5+Mk4ZhsrkuxuS6FnfgErRVaJ3hlx68wkncZzbsM5zyyJe+Bn/tOmYHe0ellBqoTUWmBmsnwtS7lkHLMFQ/cxMPRWhP03aR08UxUCmCWplAqniD5qVdXvDGeH4acvZ3hkxujuH75pnD16RjPb66juVJuBgkhxP1WTcAK0bb63/md3+Gtt97i/fffZ2BggEwmQzqdZu/evXzhC1+gra1t2jlaazwvurAJF9Ad9A7LsvjKV77Cf/7P/5lbt27xa7/2a9OOp1KpsqUOKisr+Tf/5t/wu7/7u1y+fJlf+ZVfmXa8oaGBn//5n1/wPIQQK0e7JYonPsC9cgF0+fcPs7ElamBV37jMsxNCCLEWKcsitms/ztZduJfOUjp3Au2Wr+V9vzCfI8zn8G/3TnlChZGunAxbazGrazCq6zAqKufsLL6QQHS5V/uuZUpplMqzrSE97XHXDxnJu9GPnHv357MFVLOJ6sJGwS1Tygw4ljHZTOte6FqTtKXMwCqmPQ+v+zKli2cIx0fnHGs2NJP81I9hJNNzjltsWmsyJZ+BTIn+TImBTJHhnEs4SwicdCyeaa9he0NaAn8hhJjFsgese/fu5c/+7M9mPZ5Op/nc5z7H5z73uQU9X2Nj45zPN5etW7fyta99jW9/+9t88sknd1fL7t27l3/1r/4VGzaUv4t46NAhfvM3f5Nvf/vbXLhwAYjuOj/99NP8y3/5L6mqqnqo+Qghlplp4t+8XjZcNSqriXc+i7WxXS4khRBCPDBl28T2duJs3x1tDb7ZQzg+Wn578Fy0JsyMR420JksMAGCamJXVUehaU3t35atKLl99VxEFoM2V8Wkr+rTW5NxgeuiacxkreLMGWLNx/ZC+ybqwU6VjFjUb9lHhFUn6Lsn+DAnbJOWYJByThG0+dg2HZqsjvFy1hsPMBKVLZ/GuXpy1QSoASmG3tuPs2IvZ3Los/15dP2QwW2IgG4WpA5kSBS+Y9zzLMDjYWsWB1ipsU+qsCiHEXFbVCtaV0NzczK/+6q8yNjbGwMAAdXV11NXVzXveli1b+I3f+A2GhoYYGRmhqalJglUh1hhlWsQ6n6Hw7g/vPRZPRA2stu5CmbI6RAghxKNRTozYvieI7XsCrTU6lyUYHyEcGyUYGyEcHyEYH4Nw/rBjmiAgGB0mGB3G657yerYTlReoqZtSZqAWIy5bepeLUop0zCIds9hUc6+0WRBqxgseI3mX4Vy0WnUk75It+Q/8GtmSTzZ1r4cEV6bXj1co4rZJcjJsvRO8Jm2TpGNNe9yS4OyhLbQMAES1l51tu3C271nSklOh1ozlPQayJfonw9SxvDdrPdXZbGtI80x7LenYYx8ZCCHEgsi75aTq6mqqq6sf+Lz6+nrq6+uXYEZCiOVgd2zDvXCKYHyU2O6DxPYcRNnOSk9LCCHEOqSUQqUrMNIV0Np+93EdBITZCcKxEYKxUcKxYYLxUcLsxJyBTTnacwmG+gmG+qe/diIVlRe4U2agqhYz0ATm47XKcSWZhoqaW6UctjXce7zkB4zmvWmh63DOxQserMzAVBpNwfMpePOHt7ZpkHQmg1c7CmJT9n2BrGERA1bb35aF1BteilrD98oAnCUcH5lzrFFVQ2znPuyO7Sh78ZtCFdyA/myRwUyJgUy0SvVh/+4oFC1VcZ5pr6WxIrbIMxVCiPVNAlYhxGNNKUXi8MuoWGzZ618JIVYnqUU500pvvV3vlGliVtVgVtVg38td0Z5HODFGMDYcBa/jUQCrC7kHfg1dyOEXcnD7xt3HfuqCy3hc0VNtot0SypFAZSXELJPmSrNsmYG7oWvOZTgflRnQDxi6z8cLQsYLIeOFORp2bX0eI9FB0i2SaG4g6bskApfU5NekP/kjliThFjFnqW2/FOZ7z17MWsNhNoN76SzulQvzlgGwNmwitnPfopYBCELNcM69u82/P1siU3zwRmt3JGyTxooYjRVxGtIxGtMxHEtWNAshxMOQgFUI8dgza+YvCyKEEEIsN2XbmHUNmHUN0x4Pi8XJsHWEcHyUYHT4oeq7Kg3VBU11wSfzv/8Qq30rzrZdmPVNUsd1hU0tM9BeO73MwFjBi2q6jlwnbzoULId8KkbeCyh4waIHsHeEhkE2niQbr5h9kNMCQNwrkWisJxl4JP0SycAlcSeErWog6RZJGia21qv+75rWmqD/Fu7FM3g3e+YpA+Bgb92Fs2MPZsWjlY/TWpMt+VETqmyJwUyJoVyJIHy4P19DKepSDo0VcZoqYjRWxKiIWav+/78QQqwVErAKIYQQQogZVmrrrZifEY9jxDdgNd1ryKq1RudzUeg6NvLA9V114ONdu4h37SJGVS3Ott3Ym7dhxKR262piGlFIVpdyYLj73oHOViCqv1n0AvJuFLbm3YD8tF/70WNugB8u3SrToh2jGEsxWu6gqom+Njdjvd8TlSSYrAl7f6mC5ORj8RVo2qV9H6/rMqWLZ+YvA1BZjbNzH87mHQ9dBsALJhtRTW7178+UFlTiYTbpmH03SG1Ix6hPO1iGrE4VQoilIgGrEEIIIYQoazm33opHo5RCpdIYqTS0brr7uA5Dwsz4vfqud1a+Zidmfa5wfITi8Xconngfe9NWnG27MRtkVetaYCg12cRq/o95rh9Gq15dn9zUQNYNKHj3gtii/4AN2B6AH4ZkiuG829yVUlEAeyd4LdOwKzn5+KOGiGE2g3t5sgyAuzhlALSOWkyFWqN19DXvBneD1IFMkdGHaER1h20a1E9u8W+qiNFQESO1gL8DQgghFo+86wohhBBCCLFOKcMoX9/V93ln5I/oGAtomZhlJWMQ4HVdwuu6hFFVE61q7diOEZdVreuBYxk4lkF1Yu4Vl0GoKfz6++QvXCHvxCmoA+Qth7wVo2A65E2bvBUjbzks1ZpYrfXk6lsf5ilB7FgGSdu6G8L6pS0o5aNRfNAzcjfgDHX0vKGGUIf4Y6O4N67jDQ+i0YTaQRP9njQQotAatGlh1DehGltQsQThLU1488bd59JaE6JnvM5iqk44d4PUxnSM2pSz7Ct8hRBCTCcBqxBCCCGEEI8ZZVkMVBgMVBjEPM1rnc/iXjk/68rWcHyU4vF3KZ44ht22OVrV2tgiq1ofA6ahSPsu6ezkNvmJxrLjNFA6c468EyfvJChwMApiJ+vE5u7Ui/U9XOvhttEvhOuHuL7LWCH6deBtvHvsxI2x6XMOw2h199AgYTE/+Wj5ualYPKqJXFOHMk0IgcKD1T1+GDHrTiOqGE3pKFSNWeaSv64QQogHIwGrEEIIIYQQj7GSrYjt7cTZc5Cg7ybulQt4N7rL124NArzuK3jdVzAqqqJVrVt2yqpWgQLivkvcd6nNT0BmoPzAEyfwDZP8oafJ7/+/Z9aH9QIKbkDODSh6wUNvm5+Ldl2C4UGCkSF0MHedU6OiCrO+ESNdyVLfT1CTjaiaJuumNlbEqYpLIyohhFgLJGAVQgghhBBCoJTCatmI1bKRsFjAu3YR98oFwsx42fFhZpziJ+9TPPkhdltHtKq1acPcYdCRuRunceJe47R5xx5dnLq/D9LMbb6xIPWIF8IKAyr9IpWVcwfzU5t23Qte/Wn1Yu88vpCmXWEuSzA0QDAxBnNs21emiVFTh1nXsKSN3lKONbk6NU5TRYz6lINlSiMqIYRYiyRgFUIIIYQQQkxjxBPE9nTi7D5I0H8L98p5vN7u8qtawwCv5ypez9XJVa27Jle1JqaPO3JkeoBaTjZ77+fzjT1y5JFD1iOvH5kWoJadkntvTvONvfOcj1vIGqLxjRC/LoWeDNiVea9xlbrzXw1UJ1AaMHw49REYBkoZYBigDJQZfcWIfthKUWWYVBkKZZhgK4iZYCiU4YBSaKVwtaLoa/IB5P2QQqApeBrb6Ke2oGjKBLTGTmOgURYYChQaAzAApTRmIoXTthmntQ3TtlFKYaioeZi685XpvzYUd8fNNf7+cZahiNuy1V8IIdYLCViFEEIIIYR4DC0kBFRKYTW3YjW3EhaLeF2XolqtE2Nlx0erWo9RPPkR9sYOnG275u2wLtamEI1nhHgqJFBhlKImnXsDjFlWlFZMrgg1Azh9fFHnZAIVkz/u+MK1U3d//lLiybLnWS1tODv3YW1ok7+rQgghHooErEIIIYQQQoh5GfE4sd0HcHbtJxjsw718Hq/3GgSzrGq9fhXv+lWMdCXO1l3Yk6sFAejsLP8iU1etLmTMIupsLv96U1etzjbm/nHrVWAZ+IYfharG4tdGXU7KsrG37sTZvhezqnqlpyOEEGKNk4BVCCGEEEKIpTS1lugi1Q1dSUoprMYWrMYWwtLzeF2Xo1Wt46Nlx4fZCYonP6Boudh1KexcCQuNQlYKzmq++rOwLPVqtdaEo8N4ho/XXElom9HK0zXMqKjC2bEPZ+sOlO3Mf4IQQgixABKwCiGEEEKINUMaEq0uRixObNd+nJ37CAb7o1qt16+WX9WqwEs6eEkHw/KwQwMnNDEkaJ1uIbVqYcnq1WqtCYYH8K934fV2EWYnolB1nnqhSoOVczG9AK2Ahnvjp611zZaiOq2hAZu2QBiiwxB0CEGI1mHUgCoMINToMJj89fRx6Cm/nqNh1R39FQbJl/8PKQMghBBiSUjAKoRYGrNd6D/IhwEhhBBiCmlItHpFq1qbsRqbCZ98Dq/ryuSq1pGy40OlKZkBJSPA1gZ2aGJpWdO6UnQYEgzcxuvtwuvtRhdyCzpPabC1gXXnz29kynnhLB81R/PR1002vPiZR5x5RJcNZKNffz/3HVwTPEtht25alNcTQggh7icBqxBCCCGEEGLRRKta9+Hs3EswPIB3+Txez9Xpqxj7+u7+1Jv8Yfghtq1xMgUMP5QbsTB7HVp45Hq1OvDx+27h93bh3ehGl4oLmpLSCjs0sLWBqdWqKPWglALTAhMU9rRjudjKz08IIcT6JwGrEGLxHD06fw2whXwYuP85hRBCiPtIQ6LVTymFVd+EVd9E/MnncP/H63huQOCU32oeWgal2jSlmjRWwcUOFXbeQy1g+7dYGO15+Ld78Xq78G9eR3vugs4ztMKeKGIVXMy6TasiVBVCCCFWEwlYhRCLa75AdJ01+hBCCCHE/JQTI/bm2zhHXiZQGs8I8dRkrc6pJle2+jEHv7mZwuQWdDs0sLQxPdiT64gF0UrhqQD/7e/j3+pFB/6CzjOqarDbNmO3bcb4g++gxguTRyRcFUIIIe4nAasQQgghhBBiWaijb2ERfQiJey5ed1SrNRgZiga8/vq9wV/8InCvhICybKy2zdgd27CaNkjMN4cQja9CvPo0ftwGy4fernnPM2vro//HbZsxq2qmHJH/20IIIcRcJGAVQgghhBBCLDtlOzjb9+Bs30MwPIh75TyeZuaq1kna9/C6LuF1XULFE9htW7A7tmE2NElXeKJQ1TNCfBX9QAEJe+6TlMKsb4pWqm7swKioXJa5CiGEEOuNBKxCCCGEEEKIFWXWNZCoayDuO9HKSyPEM8yoE3wZuljAvXwW9/JZjFQFdvtW7I5tGNW1j1XYGlgGfsLBM10CY4G1apWB1dQSrVTd2IGRTC3tJIUQQojHgASsQgghhBBCiFVBobC1iR2YhP/nz+H3duP1XMHvvwWzNLsKcxlK505QOncCo6oWp2MbdvvWdbcaU6MJgUCFBEoTNFXeaxg2X7iqwWptx27rwGrtwIjHH34iJ+ZoEJfNLmycEEIIsc5IwCqEEEIIIR7K0S9KkyGxdIxYHGfbLpxtuwjzObzr1/C6rxAMD8x6Tjg+QvHkBxRPfhBtfW/fht2+BSORXMaZPzoNaNMgUEEUpipNoMLp5RPuhKuzUBqsvItV8LDrHNTLP76kcxZCCCEeZxKwCiGEEEIIIVY1I5kitms/sV37CTLjeN1X8LqvEE6MzXpOMNRPMNRP8eN3sZpbo7C1rQPlxJZx5gsTFvIEI4MEw4MEpkewoRptqqg51QNQGixtYIcGljZQw7k7Rx5tgkePwpEj84+bumq1s3P+5xRCCCHWCQlYhRBCCCGEEGuGWVGFuf9JYvsOEY4O4/VMhq35XPkTtMa/fQP/9g0KH5rYre3Y7VuxWjct78QnhaUi4chQFKZOhqphfsrWeiMEc+GBqAo0dmhihQaWVqhHDVNns5BAdGoIKwGqEEKIx4gErEIIIYQQQog1RymFWVuPWVtPrPNZgsG+aGVrz1W0Wyp/UhBEpQauX0PZDoduegy5FjfjD7ZSdKG05xKMDhMMDURh6sgQYWb8kZ5TaYWpFeZ4AavkY5Z8VGPH4kxYCCGEEA9FAlYhhBBCCCHEmqaUwmpswWpsIf7k8/h9N/C6r+Lf6Eb7XtlztOfSPhryU9kKCqYmxKe3ymA0oUA9+CpQU0O9a7JlOCD/7lGCkcGohMEszbkW9PvSYGojClS1wtQGiqgZGBPFh35eIYQQQiwuCViFEEIIIYQQ64YyJ8sAtLajPQ/vZg9e9xX82zcgDMqekwgUzcMBW4cDco7iRpVBb5VBJm6Uf41QU1nS1BQ01UVNTSHk2aEaDA3NaR+v69KDz9uyMWrrMQMTcziH6foYdZuWbsu/EEIIIRaNBKxCiOUl9biEEEIIsUyUbeN0bMPp2EZYKuJf74rC1sHbs64sTbmanYMBOwcDxuMKFcS5Gfeo8UwO3PapKYRUFTVmOP284oMsVDVNzJr6yRIHDZh1DRiV1SjDgP/n65B37/wOHur3LYQQQojlJQGrEEIIIYQQYnVYwhuxRiyOs303zvbdhPksXs81Ri/+v5Cd/ZyqouZwNgEkAGgull8BOydlYFbXTAapjVGYWlWDMs2H+40IIYQQYtWRgFUIIYQQQgjxWDGSaWK7D/DWVocuZ5ztOYcjpiJdevh6qQBawURMYW/eEQWq9Q2Y1XUo6yE/dp04Mfux7P/P3n1HR1Xn/x9/TToEQoCE0HuXtgEL0lewIwoiiAq7siiIfldxXZZdQBCBRUVZNYt+FQ0eFZCmiMtRV0oA6Z1gaKFDSALEkErK/P7gl3yJaZdhJvlc8nyc4zmemXuTJ4F7+cybmXtTrG1XlvikEgCggmLACgAAANyIPn1Kfv7a4Vdp20oMqcrYr7652h6coewwX1XLcKrBr7mq/2uuKmWVPmxN8XMoqZJDlyp56eeUy0rwy9YtdRtp4p0Wfp8BAMBNgwErAAAA4Ko+fUp/9+D1vtOwTx+GrOXB4dCvlRz6tZKXosOcqpnmVP1fc+VIdyogx6EUn1ydDfLSpUoOJVXy0qUAh7J8/u8aqWezs93TsWaNtUH8tX+WOnUq/Ws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fafbs2erSpYtGjhyZ/2KkqGsrSVc/zvbbaxl5Wnh4uMLDw3Xo0CGtXLlSmzZt0saNG1W5cmXdeuutateundq2bZu/KPaEhISEQn8uunXrpnnz5qlatWqF/lW+QYMGqly5spKSkjzWdPToUfXu3bvAwk2SQkND1a9fPy1evFh9+vTRrl27tGrVKm3cuFHDhg1Tnz59PNbkyrFXFn799VfVqVOnwJ+RqlWrqm/fvlq2bJnuvvvuAtv7+fmpQ4cOWr9+vcea6tatq2nTpumbb77RkiVL9Oabb+qpp54q1JK34P7tOwzKyunTp9WjR48iF/6dOnVS48aNK+R5ysTjz8R1golrBMnMc5WJ5ylTj79rmbBGkFgnWGXiecrEY09inWCViecpE489yczjz9R1AtyLASuMU716dcXExFje/syZM8rIyCjyOmLuNH36dM2aNUuxsbEaNmyYWrRo4dHvZ4W/v78uXLhQ5HNBQUH6xz/+oUWLFmn58uX6+9//riZNmmjnzp1q2bKlevXqVWifvAHr3LlzNWLECCUmJqpBgwYaNGiQJkyYoGrVqrnUmZmZKUn63e9+V+i5Fi1aKD093eW/cKzo06ePOnXqpA8//FDbt2/X/v371bJlS0nSsWPHilyUnDt3zuMvUorTsmVLjRs3TpcuXdL69eu1fv16rVu3TuvWrZMkhYSEKCIiwiPfu6iFSN7HtrKzs4vcx8vLy6PvnvHz85O3t3eRz+X93t155516+umn9cMPP+jrr7/WBx98oP/+97+aPn26R5qu99gbN26cRzp+q2rVqkpKSlJubm7+DUckqUOHDjp16lSRf9YvXbrk8Qv7e3l56ZFHHlHnzp0VERGhefPmaceOHRo9erSqV6/u0e9tVUl/ziSpWrVqha5N506mnqdMPP5MXCeYuEaQzDxXmXieMvX4K0p5rhEk1glWmXieMvHYy8M6oXQmnqdMPPYkM48/U9cJcC+v0jcBylbXrl11+PBhvfvuu0pOTi5x29jYWL3xxhvy9vZW//79PdoVHBysqVOnqnHjxvruu+9Up04dtW3b1tJ/ntK8eXOdOnVKO3fuLPJ5h8OhoUOH6s9//rMSExM1depUhYaGFnux8bzrsq5fv179+vXTqFGj5O/vr1mzZql79+5KS0uz1LVnzx795z//0cGDB5WVlZW/sP7tv25Kyr+GmNV/YXRV9erV9be//U1jx46Vj49P/s0ZIiMjdfLkyQLbbt68Wbt379btt9/u0abSVK9eXQ899JDefPNNzZ49W0888YTatWunX3/91WPfs2HDhtq1a5cyMjLyH/v6668lSampqdq9e3eB7U+dOqWUlBSPXlepRYsW2rJlS4GmPHl/ZmvUqCE/Pz89+OCDevfdd3X//ffr2LFjHmu6nmMvPT1d06dP1549ezzWk6dTp05KS0vTl19+WeDxNm3a6C9/+Uuh7Y8cOaJt27aV2SKvYcOGmjFjhgYNGqS9e/fqL3/5S6Gb75SV356nmjdvXuw7T3Jzc3XmzBmPD1NMPE+ZePyZuE4wcY0gmXmuMvU8ZeLxV5LyWCNIrBOsMvE8Zeqxdy3WCSUz7Txl4rEnmXn8mbpOgHs5nHm3dwQMkZmZqRkzZigmJkY+Pj5q3bq1mjRpomrVqsnf319ZWVk6f/68jh49qiNHjsjLy0tPP/20+vXrVyZ9iYmJmjBhgpo2bVrg+lPlITY2VpMmTZK3t7eeeuop9e3bVw6Ho8ht77zzTm3ZslX8iqcAACAASURBVEV9+/ZVcHCwFi1aJIfDoREjRigyMlI5OTnq37+/qlevro8//jj/AupOp1N/+MMf9NlnnykiIqLU69JMmDBBJ0+ezH8Xg4+Pj3788UclJydr4cKFevjhh/O3TUlJ0ahRo7Rw4ULNmjVLf/3rX930kylZUlKSPv74Y23bti3/saZNmyosLEyJiYk6fPiwateurRkzZigwMNBjHUXdodSKK1eueOwuwjExMZo2bZqqVKmi1q1b6/Tp0zp9+rS6deumPXv2KDc3N//C/ufOndO3336rhIQEjRw5stBHudxlz549mjFjhurXr6/BgwerefPm+Tdf+Pnnn1W/fn3Nnj270H5xcXEe+5fo6zn2oqOjNWvWrPx3cnvyztyS9Mknn+j7779X06ZN9eKLLxZ7sf4lS5ZoxYoVysnJ0YwZM9SoUSOPdv1WbGysIiIidPr0aXXv3l2BgYH6/vvvPf7zkYo+T1WuXFnJycl6/PHHC52nPvnkE23cuFFDhw7VI4884vE+yZzzlInHn8nrBJPWCJK55yrTz1MmHH8mrhEk1glWmXqeMv3YuxbrhJKZcJ4y8diTzD3+JPPWCXAvBqwwUlZWVv6FzIu7ZlOVKlUUHh6uBx54QI0bNy7TvpSUFKWnpyskJKTYFypl5cCBA5o7d64SExP12WefFfkxng8++EBjxozRyy+/rKysLMXFxRUasJbkwoULqlWrlm699db8f40sSXZ2tk6cOJH/l9Y///lPnTt3ThMnTtSrr74qSYqPj9eLL76ogwcPaufOnVq7dm2Rly3wpD179mjhwoWKjY3Nf8zf31933HGHHn/8cY9/JMnVF0+edujQIX3++ec6evSoAgIC1K1bNz311FM6dOiQ3n77baWkpBTYvlu3bvqf//kfjzatWLFCCxYsUG5uboHHAwICNHnyZDVr1syj378oVo69PIcOHVJERET+sedpa9eu1ZIlSzRhwgTVq1evyG1GjhypwMBAPffcc2rdurXHm4qSnZ2tRYsWaeXKlfm/t2Xx88n73teep44cOaIzZ86obdu2hc5TOTk5ateunSZMmFCmN5ORyv88JZl5/Jm8TjBpjSCZe66yw3mqPI8/U9cIEusEq0w9T9nh2MvDOqF05b1OMPHYk8w9/iTz1glwHwasMF5cXJwuXbqk9PR0eXl5KSAgQDVq1FBoaCgnpP8vNzdX+/btU8eOHQs9d+zYMXXo0EGdOnXSunXr5OXlpUuXLql69eqWB6ySVKdOHV25cqXYa7mVZMSIEfrss8+0Z88edejQIf/xiIgI7dy5U5988omio6PL7SMQiYmJunjxoipVqqQ6deqU2cIoISFBgYGBZXI3TXfJyMjQ9u3blZiYqGrVqqlRo0aFbmjhKadOndKaNWt05swZeXl5qV69err77rvL7AZSRSnp2CtK3rFXFnJzc+VwOIo9T547d67Iu/SWh0OHDunTTz9VcnKyR68dWJq0tDRdvHixwEdZIyIiFB4erjvuuKNc/84pr/NUHhOPvzysE0pn6rnKLuep8jj+7LhGkFgnFMe085Rdjr08rBNKV57rBJOPPcm84w83LwaswE1u8ODBWrJkiZ544olC/zI3ffp0dejQQf3791d4eLi6dOmiOnXqFHp3S05OjqpUqSIvLy+lpqZed8OcOXP00ksv6bvvvtP9999f4Ll77rlHP/zwgxITEwvd5RgAAAAAAMB0Zfv2B+A6XLlyRbGxsQoICFDDhg3z73bpdDq1e/dunTlzRgEBAerUqVOZ38U1IyNDBw4c0Pnz53XhwgVduXJF2dnZCgwMVHBwsNq3b6+GDRsa0bR//35J0hdffFHkfnv37s2/QLokffPNN3rooYcKbPPDDz8oIyNDd9xxh0tNQUFBcjgcmjBhgk6fPp3/c6pRo4bWrVunTp06ldlw1cTfO1O7aAJgspSUFB0/flw+Pj5q0qRJiTdL3L9/v2JiYvToo49WuCZTu2iyb5OpXSY2mfp6xtT1VEld1atXV7t27Vh70gQYiwErjLRx40Z9/PHH+Xetr1WrlsaNG6e6detq1qxZio6Ozt8278YN9913n8e7kpOTFRkZqS1btuRf9Lw4TZs21QsvvKC6deuWa1OHDh3yP5b/26ZrLxFw5swZNW7cWOPHj1e3bt3yB55Hjx7V888/L+nq9Zhcbapbt6727t2rd999V23atFFmZqY2bdqkzMxMDRw48IZ/Dq40Faesfu9M7aIJgOm++OKLAtfk8/f31+9//3s9+uijqlKlSqHt9+/fr+XLl3t0mGJik6ldNNm3ydQuE5tMfD1j6nrKxC6a7NsElBcGrDDO2bNn9e9//1uVK1fWAw88oJSUFG3ZskUzZsxQ586dFR0drdtuu02dO3fW5cuX9f333ysyMlLNmjVTy5YtPdYVFxenyZMnKzs7Ww8++KA6duyo2rVrq0qVKvLx8VF2drZ+/fVXHTx4UFFRUdqzZ48mTZqkd955R0FBQcY31atXT5MmTdKrr76q5s2bq0uXLsrKytLPP/+srKwsPfHEE4UGrBMnTpQkvf7666U2ZWZm6r777tOePXt07NgxZWZmKicnR7Vr19axY8eUnJxsi5/Tzd5FEwDTrVy5UitWrJC/v79+97vfKScnR/v379eqVau0ceNGPfvss+rSpUuFbzK1iyb7NpnaZWKTia9nTF1PmdhFk32bgPLEgBXG2bhxo7KzszV58mQ1aNBAkjRw4ECNHz9ea9as0V133aVnnnkmf/vevXtr/PjxWrp0qSZMmOCxrk8//VS+vr564403FBwcXOh5Pz8/hYaGKjQ0VN27d9fXX3+tBQsWaMmSJXr66adt0TR58mS1bNlSb731ljZs2KDKlSurd+/eGjVqlAYPHlxo++nTp0sqOGAtqWnbtm1677339O2338rhcGjw4MEKCwvTokWLbPVzupm7aLJm3bp1Lu/bq1cvN5YUZGIXTdaY2CSZ2bVq1Sr5+Pho2rRpatSokaSrH8NduXKlli9frjfffFMPPPCAnnzyyfyP43qaiU2mdtFk3yZTu0xsMvH1jInrKVO7aLJvk2Tm2sXEJrgfA1YYJzExUbVr185fjEhS7dq1ddddd+m7774rdH3QqlWrqnPnztq8ebNHuw4cOKB+/foV+ZdHUQYMGKBly5YpNjbW2Kai7nE3dOhQDR061NLXK2r/kpp8fX01btw4jRs3rsDX+Prrr43+OVWkLpqsiYyMzP/I3/Xy5CL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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "import matplotlib.pyplot as plt\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                  opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                  width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(\"中国平安\")\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "plt.xticks(ticks =  np.arange(0,len(df3)), labels = df3.date.dt.strftime('%Y-%m-%d').to_numpy() )\n",
    "plt.xticks(rotation=90, size=8)\n",
    "\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-01-11</th>\n",
       "      <td>24.53</td>\n",
       "      <td>24.88</td>\n",
       "      <td>24.16</td>\n",
       "      <td>24.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-10</th>\n",
       "      <td>24.80</td>\n",
       "      <td>24.92</td>\n",
       "      <td>24.31</td>\n",
       "      <td>24.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-09</th>\n",
       "      <td>25.70</td>\n",
       "      <td>25.75</td>\n",
       "      <td>24.60</td>\n",
       "      <td>24.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-06</th>\n",
       "      <td>25.65</td>\n",
       "      <td>25.94</td>\n",
       "      <td>25.55</td>\n",
       "      <td>25.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-05</th>\n",
       "      <td>25.55</td>\n",
       "      <td>25.84</td>\n",
       "      <td>25.52</td>\n",
       "      <td>25.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>14.50</td>\n",
       "      <td>14.80</td>\n",
       "      <td>13.88</td>\n",
       "      <td>14.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>13.69</td>\n",
       "      <td>14.53</td>\n",
       "      <td>13.69</td>\n",
       "      <td>14.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>13.44</td>\n",
       "      <td>13.73</td>\n",
       "      <td>13.20</td>\n",
       "      <td>13.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>13.58</td>\n",
       "      <td>13.75</td>\n",
       "      <td>13.19</td>\n",
       "      <td>13.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>13.65</td>\n",
       "      <td>13.68</td>\n",
       "      <td>13.18</td>\n",
       "      <td>13.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5410 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             open   high    low  close\n",
       "trade_date                            \n",
       "2023-01-11  24.53  24.88  24.16  24.31\n",
       "2023-01-10  24.80  24.92  24.31  24.49\n",
       "2023-01-09  25.70  25.75  24.60  24.77\n",
       "2023-01-06  25.65  25.94  25.55  25.76\n",
       "2023-01-05  25.55  25.84  25.52  25.74\n",
       "...           ...    ...    ...    ...\n",
       "2000-01-10  14.50  14.80  13.88  14.73\n",
       "2000-01-07  13.69  14.53  13.69  14.29\n",
       "2000-01-06  13.44  13.73  13.20  13.65\n",
       "2000-01-05  13.58  13.75  13.19  13.53\n",
       "2000-01-04  13.65  13.68  13.18  13.60\n",
       "\n",
       "[5410 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from matplotlib.dates  import date2num\n",
    "#data['date']=date2num(data.index.to_pydatetime())\n",
    "#data=data.sort_values(by=\"date\",ascending=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     trade_date   open   high    low  close\n",
      "0    2023-01-11  24.53  24.88  24.16  24.31\n",
      "1    2023-01-10  24.80  24.92  24.31  24.49\n",
      "2    2023-01-09  25.70  25.75  24.60  24.77\n",
      "3    2023-01-06  25.65  25.94  25.55  25.76\n",
      "4    2023-01-05  25.55  25.84  25.52  25.74\n",
      "...         ...    ...    ...    ...    ...\n",
      "5405 2000-01-10  14.50  14.80  13.88  14.73\n",
      "5406 2000-01-07  13.69  14.53  13.69  14.29\n",
      "5407 2000-01-06  13.44  13.73  13.20  13.65\n",
      "5408 2000-01-05  13.58  13.75  13.19  13.53\n",
      "5409 2000-01-04  13.65  13.68  13.18  13.60\n",
      "\n",
      "[5410 rows x 5 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5410 entries, 5409 to 0\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  5410 non-null   datetime64[ns]\n",
      " 1   open        5410 non-null   float64       \n",
      " 2   high        5410 non-null   float64       \n",
      " 3   low         5410 non-null   float64       \n",
      " 4   close       5410 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 253.6 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5409</th>\n",
       "      <td>2000-01-04</td>\n",
       "      <td>13.65</td>\n",
       "      <td>13.68</td>\n",
       "      <td>13.18</td>\n",
       "      <td>13.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5408</th>\n",
       "      <td>2000-01-05</td>\n",
       "      <td>13.58</td>\n",
       "      <td>13.75</td>\n",
       "      <td>13.19</td>\n",
       "      <td>13.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5407</th>\n",
       "      <td>2000-01-06</td>\n",
       "      <td>13.44</td>\n",
       "      <td>13.73</td>\n",
       "      <td>13.20</td>\n",
       "      <td>13.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5406</th>\n",
       "      <td>2000-01-07</td>\n",
       "      <td>13.69</td>\n",
       "      <td>14.53</td>\n",
       "      <td>13.69</td>\n",
       "      <td>14.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5405</th>\n",
       "      <td>2000-01-10</td>\n",
       "      <td>14.50</td>\n",
       "      <td>14.80</td>\n",
       "      <td>13.88</td>\n",
       "      <td>14.73</td>\n",
       "      <td>13.960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-01-05</td>\n",
       "      <td>25.55</td>\n",
       "      <td>25.84</td>\n",
       "      <td>25.52</td>\n",
       "      <td>25.74</td>\n",
       "      <td>25.522</td>\n",
       "      <td>24.871</td>\n",
       "      <td>25.765000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-01-06</td>\n",
       "      <td>25.65</td>\n",
       "      <td>25.94</td>\n",
       "      <td>25.55</td>\n",
       "      <td>25.76</td>\n",
       "      <td>25.648</td>\n",
       "      <td>25.036</td>\n",
       "      <td>25.755000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-01-09</td>\n",
       "      <td>25.70</td>\n",
       "      <td>25.75</td>\n",
       "      <td>24.60</td>\n",
       "      <td>24.77</td>\n",
       "      <td>25.512</td>\n",
       "      <td>25.130</td>\n",
       "      <td>25.727333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-01-10</td>\n",
       "      <td>24.80</td>\n",
       "      <td>24.92</td>\n",
       "      <td>24.31</td>\n",
       "      <td>24.49</td>\n",
       "      <td>25.270</td>\n",
       "      <td>25.131</td>\n",
       "      <td>25.678000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-01-11</td>\n",
       "      <td>24.53</td>\n",
       "      <td>24.88</td>\n",
       "      <td>24.16</td>\n",
       "      <td>24.31</td>\n",
       "      <td>25.014</td>\n",
       "      <td>25.111</td>\n",
       "      <td>25.598333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5410 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5      10         30\n",
       "5409 2000-01-04  13.65  13.68  13.18  13.60     NaN     NaN        NaN\n",
       "5408 2000-01-05  13.58  13.75  13.19  13.53     NaN     NaN        NaN\n",
       "5407 2000-01-06  13.44  13.73  13.20  13.65     NaN     NaN        NaN\n",
       "5406 2000-01-07  13.69  14.53  13.69  14.29     NaN     NaN        NaN\n",
       "5405 2000-01-10  14.50  14.80  13.88  14.73  13.960     NaN        NaN\n",
       "...         ...    ...    ...    ...    ...     ...     ...        ...\n",
       "4    2023-01-05  25.55  25.84  25.52  25.74  25.522  24.871  25.765000\n",
       "3    2023-01-06  25.65  25.94  25.55  25.76  25.648  25.036  25.755000\n",
       "2    2023-01-09  25.70  25.75  24.60  24.77  25.512  25.130  25.727333\n",
       "1    2023-01-10  24.80  24.92  24.31  24.49  25.270  25.131  25.678000\n",
       "0    2023-01-11  24.53  24.88  24.16  24.31  25.014  25.111  25.598333\n",
       "\n",
       "[5410 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df3 = data.reset_index().iloc[:,:]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "print(df3)\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "df3['30']=df3.close.rolling(30).mean()\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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AAAAAAAAAAAAAZJhrAQAAAAAAAAAAAAAAAAAAAAAAkDGmpKzvOjgS+QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "import matplotlib.pyplot as plt\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                  opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                  width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(\"中航西飞\")\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "plt.plot(df3['30'].values,alpha=0.5, label='MA30')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "tempXticks=np.arange(0,len(df3))\n",
    "#XticksData=data.asfreq(\"2M\").dropna()\n",
    "nameXticks =  df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "\n",
    "plt.xticks(ticks =tempXticks , labels =nameXticks )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "#修改X轴间隔\n",
    "x_major_locator=plt.MultipleLocator(100)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.spines['left'].set_color('red')\n",
    "plt.xlim(0,1100)\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "a=np.linspace(0,round(len(df3),0),10,dtype=int).tolist()\n",
    "b=df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "c=data.asfreq(\"2M\").dropna()\n",
    "#c.dropna().index\n",
    "cc=c.index.strftime('%Y-%m-%d').to_numpy()\n",
    "e=df3.trade_date.tolist()\n",
    "#f=[e[i] for i in cc]\n",
    "cc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data1=pd.DataFrame(data,columns=[\"trade_date\",\"close\"])\n",
    "#data1=pd.DataFrame(data,columns=[\"close\"])\n",
    "data1=data.iloc[::-1]\n",
    "data1[\"open\"].plot(label=\"open\")\n",
    "data1[\"close\"].plot(label=\"close\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.items of       trade_date  close\n",
       "0          24.53  24.88\n",
       "1          24.80  24.92\n",
       "2          25.70  25.75\n",
       "3          25.65  25.94\n",
       "4          25.55  25.84\n",
       "...          ...    ...\n",
       "5405       14.50  14.80\n",
       "5406       13.69  14.53\n",
       "5407       13.44  13.73\n",
       "5408       13.58  13.75\n",
       "5409       13.65  13.68\n",
       "\n",
       "[5410 rows x 2 columns]>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddd=[]\n",
    "for item in data1.items():\n",
    "    ddd.append(item)\n",
    "ind=ddd[0][1].values\n",
    "da1=ddd[1][1].values\n",
    "index1=[]\n",
    "data2=[]\n",
    "for item in ind:\n",
    "    index1.append(item)\n",
    "index1.reverse()\n",
    "for item in da1:\n",
    "    data2.append(item)\n",
    "data2.reverse()\n",
    "data1={'trade_date':index1,'close':data2}\n",
    "stockdata=pd.DataFrame(data1)\n",
    "\n",
    "stockdata.items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13.68, 13.75, 13.73, ...,  9.65, 10.6 , 10.69])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.iloc[0:3000,1:3].values[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.dates  import date2num\n",
    "date2num(df3.trade_date.values)\n",
    "df3['date']=date2num(df3.trade_date.values)\n",
    "#df3=df3.sort_values(by=\"date\",ascending=True)\n",
    "\n",
    "#df3=df3[['date','open', 'high', 'low', 'close']]\n",
    "ind=np.arange(0,3000)\n",
    "dataf1=df3[['date','open', 'high', 'low', 'close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>730123.0</th>\n",
       "      <td>13.65</td>\n",
       "      <td>13.68</td>\n",
       "      <td>13.18</td>\n",
       "      <td>13.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730124.0</th>\n",
       "      <td>13.58</td>\n",
       "      <td>13.75</td>\n",
       "      <td>13.19</td>\n",
       "      <td>13.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730125.0</th>\n",
       "      <td>13.44</td>\n",
       "      <td>13.73</td>\n",
       "      <td>13.20</td>\n",
       "      <td>13.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730126.0</th>\n",
       "      <td>13.69</td>\n",
       "      <td>14.53</td>\n",
       "      <td>13.69</td>\n",
       "      <td>14.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730129.0</th>\n",
       "      <td>14.50</td>\n",
       "      <td>14.80</td>\n",
       "      <td>13.88</td>\n",
       "      <td>14.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738525.0</th>\n",
       "      <td>25.55</td>\n",
       "      <td>25.84</td>\n",
       "      <td>25.52</td>\n",
       "      <td>25.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738526.0</th>\n",
       "      <td>25.65</td>\n",
       "      <td>25.94</td>\n",
       "      <td>25.55</td>\n",
       "      <td>25.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738529.0</th>\n",
       "      <td>25.70</td>\n",
       "      <td>25.75</td>\n",
       "      <td>24.60</td>\n",
       "      <td>24.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738530.0</th>\n",
       "      <td>24.80</td>\n",
       "      <td>24.92</td>\n",
       "      <td>24.31</td>\n",
       "      <td>24.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738531.0</th>\n",
       "      <td>24.53</td>\n",
       "      <td>24.88</td>\n",
       "      <td>24.16</td>\n",
       "      <td>24.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5410 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           open   high    low  close\n",
       "date                                \n",
       "730123.0  13.65  13.68  13.18  13.60\n",
       "730124.0  13.58  13.75  13.19  13.53\n",
       "730125.0  13.44  13.73  13.20  13.65\n",
       "730126.0  13.69  14.53  13.69  14.29\n",
       "730129.0  14.50  14.80  13.88  14.73\n",
       "...         ...    ...    ...    ...\n",
       "738525.0  25.55  25.84  25.52  25.74\n",
       "738526.0  25.65  25.94  25.55  25.76\n",
       "738529.0  25.70  25.75  24.60  24.77\n",
       "738530.0  24.80  24.92  24.31  24.49\n",
       "738531.0  24.53  24.88  24.16  24.31\n",
       "\n",
       "[5410 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习拟合股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[13.6 ]\n",
      " [13.53]\n",
      " [13.65]\n",
      " [14.29]\n",
      " [14.73]\n",
      " [15.08]\n",
      " [14.45]\n",
      " [14.1 ]\n",
      " [13.48]\n",
      " [13.36]\n",
      " [13.27]\n",
      " [14.18]\n",
      " [14.02]\n",
      " [14.38]\n",
      " [14.99]\n",
      " [14.51]\n",
      " [14.44]\n",
      " [14.78]\n",
      " [15.6 ]\n",
      " [17.04]\n",
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      " [ 7.98]\n",
      " [ 7.81]]\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "#拟合开始\n",
    "#\n",
    "training_set = dataf1.iloc[0:512, 4:5].to_numpy()#要提取一列数据，否则会出错\n",
    "test_set     = dataf1.iloc[973 - 300:, 4:5].to_numpy()\n",
    "print(training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据归一化，范围是0到1\n",
    "sc  = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled  = sc.transform(test_set) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前 n_timestamp 天的数据为 X；n_timestamp+1天数据为 Y。\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        \n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "            \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train          = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "X_test, y_test   = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test           = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " bidirectional (Bidirectiona  (None, 100)              20800     \n",
      " l)                                                              \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 101       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 20,901\n",
      "Trainable params: 20,901\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#五、构建模型\n",
    "# 建构 LSTM模型\n",
    "model_type=3\n",
    "if model_type == 1:\n",
    "    # 单层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu',\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(units=1))\n",
    "if model_type == 2:\n",
    "    # 多层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(units=50, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "if model_type == 3:\n",
    "    # 双向 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(50, activation='relu'),\n",
    "                            input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "model.summary()  # 输出模型结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "8/8 [==============================] - 4s 106ms/step - loss: 0.2556 - val_loss: 1.1244\n",
      "Epoch 2/30\n",
      "8/8 [==============================] - 0s 32ms/step - loss: 0.1737 - val_loss: 0.8046\n",
      "Epoch 3/30\n",
      "8/8 [==============================] - 0s 32ms/step - loss: 0.1051 - val_loss: 0.4991\n",
      "Epoch 4/30\n",
      "8/8 [==============================] - 0s 28ms/step - loss: 0.0495 - val_loss: 0.2210\n",
      "Epoch 5/30\n",
      "8/8 [==============================] - 0s 36ms/step - loss: 0.0142 - val_loss: 0.0490\n",
      "Epoch 6/30\n",
      "8/8 [==============================] - 0s 32ms/step - loss: 0.0077 - val_loss: 0.0310\n",
      "Epoch 7/30\n",
      "8/8 [==============================] - 0s 31ms/step - loss: 0.0069 - val_loss: 0.0252\n",
      "Epoch 8/30\n",
      "8/8 [==============================] - 0s 32ms/step - loss: 0.0044 - val_loss: 0.0261\n",
      "Epoch 9/30\n",
      "8/8 [==============================] - 0s 31ms/step - loss: 0.0036 - val_loss: 0.0292\n",
      "Epoch 10/30\n",
      "8/8 [==============================] - 0s 29ms/step - loss: 0.0025 - val_loss: 0.0434\n",
      "Epoch 11/30\n",
      "8/8 [==============================] - 0s 30ms/step - loss: 0.0018 - val_loss: 0.0510\n",
      "Epoch 12/30\n",
      "8/8 [==============================] - 0s 28ms/step - loss: 0.0014 - val_loss: 0.0770\n",
      "Epoch 13/30\n",
      "8/8 [==============================] - 0s 29ms/step - loss: 0.0011 - val_loss: 0.0871\n",
      "Epoch 14/30\n",
      "8/8 [==============================] - 0s 32ms/step - loss: 0.0011 - val_loss: 0.0974\n",
      "Epoch 15/30\n",
      "8/8 [==============================] - 0s 31ms/step - loss: 0.0011 - val_loss: 0.0936\n",
      "Epoch 16/30\n",
      "8/8 [==============================] - 0s 31ms/step - loss: 0.0011 - val_loss: 0.0879\n",
      "Epoch 17/30\n",
      "8/8 [==============================] - 0s 30ms/step - loss: 0.0010 - val_loss: 0.0829\n",
      "Epoch 18/30\n",
      "8/8 [==============================] - 0s 33ms/step - loss: 0.0010 - val_loss: 0.0802\n",
      "Epoch 19/30\n",
      "8/8 [==============================] - 0s 30ms/step - loss: 0.0010 - val_loss: 0.0787\n",
      "Epoch 20/30\n",
      "8/8 [==============================] - 0s 30ms/step - loss: 0.0010 - val_loss: 0.0752\n",
      "Epoch 21/30\n",
      "8/8 [==============================] - 0s 36ms/step - loss: 0.0010 - val_loss: 0.0771\n",
      "Epoch 22/30\n",
      "8/8 [==============================] - 0s 34ms/step - loss: 0.0010 - val_loss: 0.0742\n",
      "Epoch 23/30\n",
      "8/8 [==============================] - 0s 33ms/step - loss: 0.0010 - val_loss: 0.0761\n",
      "Epoch 24/30\n",
      "8/8 [==============================] - 0s 33ms/step - loss: 0.0010 - val_loss: 0.0726\n",
      "Epoch 25/30\n",
      "8/8 [==============================] - 0s 31ms/step - loss: 0.0010 - val_loss: 0.0723\n",
      "Epoch 26/30\n",
      "8/8 [==============================] - 0s 29ms/step - loss: 0.0010 - val_loss: 0.0704\n",
      "Epoch 27/30\n",
      "8/8 [==============================] - 0s 30ms/step - loss: 0.0010 - val_loss: 0.0703\n",
      "Epoch 28/30\n",
      "8/8 [==============================] - 0s 29ms/step - loss: 0.0010 - val_loss: 0.0705\n",
      "Epoch 29/30\n",
      "8/8 [==============================] - 0s 28ms/step - loss: 0.0010 - val_loss: 0.0691\n",
      "Epoch 30/30\n",
      "8/8 [==============================] - 0s 29ms/step - loss: 0.0010 - val_loss: 0.0692\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " bidirectional (Bidirectiona  (None, 100)              20800     \n",
      " l)                                                              \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 101       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 20,901\n",
      "Trainable params: 20,901\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss='mean_squared_error')  # 损失函数用均方误差\n",
    "#七、训练模型\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=64,\n",
    "                    epochs=n_epochs,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    validation_freq=1)  # 测试的epoch间隔数\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "#plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "148/148 [==============================] - 1s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "predicted_stock_price = model.predict(\n",
    "    X_test)                        # 测试集输入模型进行预测\n",
    "predicted_stock_price = sc.inverse_transform(\n",
    "    predicted_stock_price)  # 对预测数据还原---从（0，1）反归一化到原始范围\n",
    "real_stock_price = sc.inverse_transform(y_test)  # 对真实数据还原---从（0，1）反归一化到原始范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出真实数据和预测数据的对比曲线\n",
    "plt.plot(real_stock_price, color='red', label='Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
    "plt.title('中航西飞')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差: 8.43275\n",
      "均方根误差: 2.90392\n",
      "平均绝对误差: 1.32567\n",
      "R2: 0.92785\n"
     ]
    }
   ],
   "source": [
    "MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)\n",
    "RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5\n",
    "MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)\n",
    "R2 = metrics.r2_score(predicted_stock_price, real_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.3 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
